Cricket, one of the most popular also widely followed sports globally, has garnered significant interest from enthusiasts and analysts. Predicting the outcome of cricket matches poses a challenging yet intriguing problem, with implications for sports enthusiasts, betting markets, and team strategies. This study delves into applying Machine Learning (ML) and Data Mining techniques to forecast cricket match results. By harnessing historical match data encompassing various facets of gameplay, including player statistics, weather conditions, venue characteristics, and toss outcomes, this research endeavors to build robust predictive models. Integrating ML algorithms, such as Decision Trees (DT), Support Vector Machines (SVM), Naive Bayes (NB), and Random Forest (RF), alongside sophisticated Data Mining methodologies promises to unlock hidden patterns and insights within the data. The aim is to develop accurate and reliable predictive models that can assist stakeholders in making informed decisions related to cricket match outcomes. In our research, the ML classifier’s accuracy is accordingly NB 91%, DT 90%, SVM 85%, and RF 89%. The highest AUC value achieved by the NB classifier was 93%. The NB classifier performs best in predicting the result of our research. The findings of this study provide valuable insights for coaches, analysts, and betting enthusiasts in the cricketing community.