Authors : Ruhul Quddus Majumder

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/2y36t34d

Scribd : https://tinyurl.com/3svp3bec

DOI : https://doi.org/10.38124/ijisrt/25apr1899

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Abstract : Machine Learning has become an integral part of predictive analysis, empowering organizations to identify and analyze trends, uncover patterns, and make data-driven decisions across diverse domains. This review explores the evolution of machine learning procedures in predictive analysis and advancements, emerging trends and future scope. The deployment of predictive analysis techniques, highlighted, along with the usage of machine learning technologies for predicted modelling and the many possibilities for prediction analysis in various arenas. This paper also discusses what the emerging and future domains are where machine learning can be used for automation and maximizing the output. The evolution of machine learning (ML) and deep learning (DL) and their application in predictive data investigation has deeply influenced as it can derive data-driven insights. This paper also discusses how predictive analysis can be used to optimize security concerns and vulnerabilities and how it can detect and predict threats in the system.

Keywords : Predictive Analysis, Machine Learning, Artificial Intelligence, Data Driven Insights, Supervised Learning, Unsupervised Learning, Deep Learning, Neural Networks, Supervised Learning.

References :

1. P. L. Bokonda, K. Ouazzani-Touhami, and N. Souissi, “Predictive analysis using machine learning: Review of trends and methods,” in 2020 International Symposium on Advanced Electrical and Communication Technologies (ISAECT), 2020, pp. 1–6. doi: 10.1109/ISAECT50560.2020.9523703.
2. N. Abid, “Empowering Cybersecurity : Optimized Network Intrusion Detection Using Data Balancing and Advanced Machine Learning Models,” TIJER, vol. 11, no. 12, 2024.
3. M. Soori, B. Arezoo, and R. Dastres, “Artificial intelligence, machine learning and deep learning in advanced robotics, a review,” Cognitive Robotics. 2023. doi: 10.1016/j.cogr.2023.04.001.
4. S. Singh, “Enhancing Observability and Reliability in Wireless Networks with Service Mesh Technologies,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 5, no. 1, 2025, doi: 10.48175/568.
5. V. Kumar and M. L., “Predictive Analytics: A Review of Trends and Techniques,” Int. J. Comput. Appl., 2018, doi: 10.5120/ijca2018917434.
6. D. D. Rao1 et al., “Strategizing IoT Network Layer Security Through Advanced Intrusion Detection Systems and AI-Driven Threat Analysis,” J. Intell. Syst. Internet Things, vol. 12, no. 02, doi: https://doi.org/10.54216/JISIoT.120215.
7. K. M. R. Seetharaman, “Internet of Things (IoT) Applications in SAP: A Survey of Trends, Challenges, and Opportunities,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 3, no. 2, 2021, doi: DOI: 10.48175/IJARSCT-6268B.
8. M. S. Samarth Shah, “Deep Reinforcement Learning For Scalable Task Scheduling In Serverless Computing,” Int. Res. J. Mod. Eng. Technol. Sci., vol. 3, no. 12, pp. 1845–1852, 2021, doi: DOI : https://www.doi.org/10.56726/IRJMETS17782.
9. V. Prajapati, “Role of Identity and Access Management in Zero Trust Architecture for Cloud Security : Challenges and Solutions,” pp. 6–18, 2025, doi: 10.48175/IJARSCT-23902.
10. M. S. Akaash Vishal Hazarika, “Blockchain-based Distributed AI Models: Trust in AI model sharing,” Int. J. Sci. Res. Arch., vol. 13, no. 2, pp. 3493–3498, 2024.
11. V. Pillai, “Enhancing Transparency and Understanding in AI Decision-Making Processes,” irejournals, vol. 8, no. 1, p. 5, 2024.
12. S. Duary, P. Choudhury, S. Mishra, V. Sharma, D. D. Rao, and A. Paul Aderemi, “Cybersecurity Threats Detection in Intelligent Networks using Predictive Analytics Approaches,” in 2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM), IEEE, Feb. 2024, pp. 1–5. doi: 10.1109/ICIPTM59628.2024.10563348.
13. Godavari Modalavalasa, “The Role of DevOps in Streamlining Software Delivery: Key Practices for Seamless CI/CD,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 1, no. 12, pp. 258–267, Jan. 2021, doi: 10.48175/IJARSCT-8978C.
14. S. Chatterjee, “Risk Management in Advanced Persistent Threats (APTs ) for Critical Infrastructure in the Utility Industry,” Int. J. Multidiscip. Res., vol. 3, no. 4, pp. 1–10, 2021.
15. V. S. Thokala, “Improving Data Security and Privacy in Web Applications : A Study of Serverless Architecture,” Int. Res. J., vol. 11, no. 12, pp. 74–82, 2024.
16. M. Shah, P. Shah, and S. Patil, “Secure and Efficient Fraud Detection Using Federated Learning and Distributed Search Databases,” in 2025 IEEE 4th International Conference on AI in Cybersecurity (ICAIC), 2025, pp. 1–6. doi: 10.1109/ICAIC63015.2025.10849280.
17. A. Goyal, “Optimising Software Lifecycle Management through Predictive Maintenance : Insights and Best Practices,” Int. J. Sci. Res. Arch., vol. 07, no. 02, pp. 693–702, 2022.
18. S. B. Shah, “Machine Learning for Cyber Threat Detection and Prevention in Critical Infrastructure,” J. Glob. Res. Electron. Commun., vol. 2, no. 2, pp. 1–7, 2025, doi: https://jgrec.info/index.php/jgrec.
19. A. Gogineni, “Artificial intelligence-Driven Fault Tolerance Mechanisms for Distributed Systems Using Deep Learning Model,” J. Artif. Intell. Mach. Learn. Data Sci., vol. 1, no. 4, 2023.
20. S. R. Thota, S. Arora, and S. Gupta, “Hybrid Machine Learning Models for Predictive Maintenance in Cloud-Based Infrastructure for SaaS Applications,” in 2024 International Conference on Data Science and Network Security (ICDSNS), IEEE, Jul. 2024, pp. 1–6. doi: 10.1109/ICDSNS62112.2024.10691295.
21. S. Murri, “Data Security Environments Challenges and Solutions in Big Data,” Int. J. Curr. Eng. Technol., vol. 12, no. 6, pp. 565–574, 2022.
22. A. Goyal, “Optimising Cloud-Based CI/CD Pipelines: Techniques for Rapid Software Deployment,” Tech. Int. J. Eng. Res., vol. 11, no. 11, pp. 896–904, 2024.
23. M. Gopalsamy and K. B. Dastageer, “The Role of Ethical Hacking and AI in Proactive Cyber Defense : Current Approaches and Future Perspectives,” Int. J. Innov. Sci. Res. Technol., vol. 10, no. 2, 2025, doi: https://doi.org/10.5281/zenodo.14916984.
24. J. Thomas, “The Effect and Challenges of the Internet of Things (IoT) on the Management of Supply Chains,” Int. J. Res. Anal. Rev., vol. 8, no. 3, pp. 874–878, 2021.
25. S. S. S. Neeli, “Optimizing Database Management with DevOps: Strategies and Real-World Examples,” J. Adv. Dev. Res., vol. 11, no. 1, p. 8, 2020.
26. K. Rajchandar, M. Ramesh, A. Tyagi, S. Prabhu, D. S. Babu, and A. Roniboss, “Edge Computing in Network-based Systems: Enhancing Latency-Sensitive Applications,” in 2024 7th International Conference on Contemporary Computing and Informatics (IC3I), 2024, pp. 462–467. doi: 10.1109/IC3I61595.2024.10828607.
27. Patrick Azuka Okeleke, Daniel Ajiga, Samuel Olaoluwa Folorunsho, and Chinedu Ezeigweneme, “Predictive analytics for market trends using AI: A study in consumer behavior,” Int. J. Eng. Res. Updat., vol. 7, no. 1, pp. 036–049, Aug. 2024, doi: 10.53430/ijeru.2024.7.1.0032.
28. A. K. Aftab Arif, Muhammad Ismaeel Khan, “An overview of cyber threats generated by AI,” Int. J. Multidiscip. Sci. Arts, vol. 3, no. 4, pp. 67–76, 2024.
29. V. Pillai, “Anomaly Detection in Financial and Insurance Data-Systems,” J. AI-Assisted Sci. Discov., vol. 4, no. 2, 2024.
30. A. Gogineni, “Advancing Kubernetes Network Stack for High-Performance AI/ML Workloads,” Int. J. Sci. Technol., vol. 15, no. 4, 2024.
31. B. Boddu, “Securing and Managing Cloud Databases for Business - Critical Applications,” J. Eng. Appl. Sci. Technol., 2025.
32. S. Chatterjee, “Integrating Identity and Access Management for Critical Infrastructure : Ensuring Compliance and Security in Utility Systems,” Int. J. Innov. Res. Creat. Technol., vol. 8, no. 2, pp. 1–8, 2022.
33. A. R. A. K. Muhammad Ismaeel Khan, Aftab Arif, “The Most Recent Advances and Uses of AI in Cybersecurity,” BULLET, vol. 3, no. 4, pp. 566–578, 2024.
34. V. Pillai, “Leveraging the power of Data Analysis in Automobile and Financial industries,” Int. J. Eng. Comput. Sci., vol. 12, no. 12, 2023.
35. A. Kushwaha, P. Pathak, and S. Gupta, “Review of optimize load balancing algorithms in cloud,” Int. J. Distrib. Cloud Comput., vol. 4, no. 2, pp. 1–9, 2016.
36. S. S. S. Neeli, “Critical Cybersecurity Strategies for Database Protection against Cyber Attacks,” J. Artif. Intell. Mach. Learn. Data Sci., vol. 1, no. 1, p. 5, 2023.
37. H. S. Chandu, “Advanced Methods for Verifying Memory Controllers in Modern Computing Systems,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 4, no. 2, pp. 377–388, 2024, doi: DOI: 10.48175/IJARSCT-19862.
38. S. P. M Shah, “AI/ML Techniques for Real-Time Fraud Detection,” DZone, 2025.
39. A. Arif, M. I. Khan, A. R. A. Khan, N. Anjum, and H. Arif, “AI-Driven Cybersecurity Predictions : Safeguarding California ’ s Digital Landscape,” no. 1, pp. 74–78, 2025.
40. N. L. Rane, S. K. Mallick, Ö. Kaya, and J. Rane, “Emerging trends and future directions in machine learning and deep learning architectures,” in Applied Machine Learning and Deep Learning: Architectures and Techniques, Deep Science Publishing, 2024. doi: 10.70593/978-81-981271-4-3_10.
41. A. Singla and H. Jangir, “A Comparative Approach to Predictive Analytics with Machine Learning for Fraud Detection of Realtime Financial Data,” in 2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3), IEEE, Feb. 2020, pp. 1–4. doi: 10.1109/ICONC345789.2020.9117435.
42. R. Alfred, “The rise of machine learning for big data analytics,” in 2016 2nd International Conference on Science in Information Technology (ICSITech), IEEE, Oct. 2016, pp. 1–1. doi: 10.1109/ICSITech.2016.7852593.
43. Z. Han, “Research on Big Data Mining Application of Internet of Things Based on Artificial Intelligence Technology,” in 2022 International Conference on Computing, Robotics and System Sciences (ICRSS), IEEE, Dec. 2022, pp. 74–77. doi: 10.1109/ICRSS57469.2022.00025.
44. C. Ye, “Evolution and Application of Artificial Intelligence Art Design Based on Machine Learning Algorithm,” in 2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE), IEEE, Sep. 2021, pp. 293–297. doi: 10.1109/ICISCAE52414.2021.9590775.
45. A. D. Dhaygude, R. A. Varma, P. Yerpude, S. K. Swarnkar, R. Kumar Jindal, and F. Rabbi, “Deep Learning Approaches for Feature Extraction in Big Data Analytics,” in 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), IEEE, Dec. 2023, pp. 964–969. doi: 10.1109/UPCON59197.2023.10434607.
46. P. Nedungadi, S. Surendran, K.-Y. Tang, and R. Raman, “Big Data and AI Algorithms for Sustainable Development Goals: A Topic Modeling Analysis,” IEEE Access, pp. 1–1, 2024, doi: 10.1109/ACCESS.2024.3516500.
47. S. Gupta and S. Joshi, “Predictive Analytic Techniques for enhancing marketing performance and Personalized Customer Experience,” in International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022 - Proceedings, 2022. doi: 10.1109/IIHC55949.2022.10060286.