Mortgage-backed securities (MBSs) have long been a staple in financial markets, offering investors exposure to the residential mortgage market. However, the prepayment risk associated with these securities presents a significant challenge for investors and issuers alike. Traditional models for predicting prepayment risk often fall short in capturing the complex dynamics of the mortgage market, leading to suboptimal risk management strategies. In this research paper, we propose a novel machine learning approach to predict MBS prepayment risk with the aim of enhancing risk mitigation strategies. Leveraging advanced techniques in data analytics and predictive modeling, our methodology integrates a diverse set of features, including borrower characteristics, economic indicators, and market trends, to generate accurate forecasts of prepayment behavior. Through comprehensive experimentation and validation using historical MBS data, we demonstrate the effectiveness of our approach in accurately predicting prepayment risk across various market conditions. Our results indicate significant improvements in prediction accuracy compared to traditional models, thereby empowering investors and issuers with valuable insights for making informed decisions and mitigating risk exposures. Our study contributes to the growing body of literature on MBS prepayment risk management by showcasing the potential of machine learning techniques to provide more accurate and reliable predictions, thereby enabling stakeholders to navigate the complexities of the mortgage market with greater confidence and efficiency.