This research introduces an innovative approach for efficiently monitoring objects in computer vision systems through combining machine learning (ML) algorithms and optimization strategies. Tracking objects is crucial in applications like surveillance, self-driving vehicles, and augmented reality. By harnessing advanced ML methods such as deep learning and integrating optimization strategies including gradient-based techniques, the proposed system aims to enhance accuracy and real-time performance during tracking. The collaborative synergy between ML algorithms and optimization methods empowers the system to dynamically adjust to diverse and challenging scenarios, ensuring resilient tracking across varying environments. Experimental results underscore the efficacy of the proposed methodology, demonstrating superior tracking precision compared to conventional approaches. This study contributes to the progression of computer vision applications, providing a scalable and adaptable solution for real-world challenges in object tracking.