The landscape of software development has seen a massive shift in the last few years, with rising use of data-driven methods for making product decisions. One area that has made a significant difference is the integration of machine learning and artificial intelligence technologies to inform software engineering practice, including prioritization of product features. Software product feature prioritization is an essential process directly influencing the competitiveness and success of a product. Traditional techniques, though fundamental, tend to fall short in resolving the intricacies of contemporary software ecosystems. This study delves into the revolutionary potential of machine learning (ML) and artificial intelligence (AI) for improving feature prioritization. An extensive literature survey identifies existing trends and their drawbacks, such as inadequate integrated frameworks and scalability and interpretability issues. The suggested framework integrates heterogeneous sources of data, predictive analytics, natural language processing (NLP), and optimization algorithms to support real-time data-driven decision-making.