This study explores how machine learning (ML) can enhance decision-making in cricket betting. With the availability of large datasets and advanced analytical tools, ML offers valuable insights into predicting outcomes. The research focuses on using predictive modeling in sports analytics for betting purposes. It highlights the utilization of statistical models, data mining techniques, and machine learning algorithms to study past data and provides forecasts related to match outcomes, player performances, and other relevant factors. We introduce a Team Performance Model using seven IPL attributes that achieve 95% accuracy. Additionally, a Player Performance Model is developed with twenty-one attributes, reaching 99.74% accuracy. These models help forecast match results and individual player performances. The paper also discusses the use of Random Forest Classifier Regressor for decision-making. Strategies like value betting, portfolio optimization, and risk management are explored to boost profitability. By using these data-driven methods, bettors can make more informed and strategic bets. Overall, the study highlights the effectiveness of ML in improving betting outcomes in cricket.