Artificial intelligence (AI) has become a potent tool for resolving complicated issues in a variety of fields. This study investigates how computational and linear programming approaches, in particular, may be used to solve practical problems. The computational side of AI includes creating models and algorithms that replicate human reasoning and decision-making processes. A branch of mathematical optimization known as "linear programming" tries to maximize or minimize a linear objective function while taking into account a number of linear constraints. Combining these strategies will enable us to maximize efficiency, increase decision-making, and better allocate resources across a variety of industries, including banking, logistics, healthcare, and manufacturing. The theoretical underpinnings of computational and linear programming approaches are examined in this study, and case studies showing how they have been successfully used in practical scenarios. The results highlight the effectiveness of AI-driven computational and linear programming approaches in tackling complex problems, ultimately leading to improved outcomes and increased productivity. As AI continues to advance, further research and innovation in these areas hold great promise for solving even more intricate challenges in the future.

Keywords : Computational, Linear Programming, Artificial Intelligence.