Authors : P Shiva Naga Raju; P Nikhitha

Volume/Issue : Volume 10 - 2025, Issue 7 - July

Google Scholar : https://tinyurl.com/mt4bxv4p

Scribd : https://tinyurl.com/yc4wu2ww

DOI : https://doi.org/10.38124/ijisrt/25jul735

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Abstract : This electronic document represents a smart, proactive vehicle maintenance predictor system designed to transform the traditional ownership experience by optimizing performance, reducing downtime, and enhancing safety. By integrating advanced data analytics, machine learning, and IoT technology, the system continuously monitors critical vehicle parameters such as engine oil and filter, air cleaner filter, fuel filter, coolant, and more. The goal is to bring a data-driven, user-centric approach to vehicle maintenance and performance monitoring. Real-time data is collected via sensors and visualized through an intuitive mobile or interactive web application, which also issues alerts for issues like overheating or low oil levels. The system applies predictive maintenance techniques using historical data to forecast potential problems and schedule service tasks based on usage patterns and manufacturer guidelines. It maintains a log of previous services and sends automated reminders for upcoming maintenance. Additionally, the integration of telematics enables tracking of driving behavior to promote eco-friendly habits and record fuel efficiency and trip history. A simulation model was built using Python libraries and the Twilio API to demonstrate the concept. It tracks parameters like speed, fuel level, and gear status, triggering maintenance alerts—such as engine oil changes every 2000 km and gear oil changes every 6000 km—along with real-time notifications. The system effectively showcases the use of predictive analytics and real-time communication to ensure timely maintenance, improve reliability, and lower long-term vehicle repair costs. With automated reminders, comprehensive maintenance logs, and intelligent analysis of driving behavior, it supports better decision-making for vehicle owners. The integration with telematics not only enhances maintenance precision but also encourages eco-friendly driving by analyzing acceleration, braking, and speed patterns. Overall, the project demonstrates a scalable and impactful solution for smart vehicle management.

Keywords : Predictive Maintenance, IoT, Telematics, Machine Learning, RealTime Monitoring, Streamlit, Maintenance Scheduling.

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