This paper presents an optimization approach for metal sheet cutting processes using Integer Linear Programming (ILP). It addresses the critical challenges of material waste and inefficiencies inherent in traditional manual cutting methods. The primary objective was to develop and implement an ILP-based model to automate and optimize cutting plans, thereby minimizing material waste and enhancing production efficiency. The proposed model extends existing optimization frameworks by incorporating constraints and decision variables tailored to the Two-Dimensional Strip Packing Problem (2D-SPP). Implemented in Python with libraries such as PuLP for mathematical modeling and Matplotlib for result visualization, the model was validated using real-world datasets. Results demonstrated substantial improvements, achieving material utilization rates of up to 87.94%. These findings underscore the effectiveness of ILP in addressing complex industrial challenges, offering a systematic approach to waste reduction and workflow optimization. The paper concludes by evaluating the model’s practical implications and its potential scalability for broader industrial applications.