<p>Portfolio management in the automotive industry is crucial for optimizing resource allocation, risk management, and financial performance. This paper presents a novel approach to Portfolio Management and Optimization by integrating Multi-Objective Optimization (MOO) with Deep Learning (DL) algorithms. Despite MOO and DL being established methodologies in portfolio optimization, our study emphasizes their innovative application and potential enhancements over existing strategies. This work addresses the critical challenges in portfolio management by providing a comprehensive performance assessment through relevant case studies, highlighting significant findings and contributions to this field. The Paper demonstrate how the proposed framework improves decision-making processes in asset allocation, ultimately aiming to optimize returns while managing risk.</p>