This study explores state-of-the-art advanced ensemble learning methodologies for predictive modeling in marathon running times. The research emphases on enhancing the precision and reliability of marathon time predictions by utilizing a varied array of machine learning methods, given the multifaceted nature of influencing factors, including historical performance data. The process initiates with the deployment of individual machine learning models, followed by the application of normalization techniques to standardize the dataset, ensuring uniform feature scaling. To further refine model performance, ensemble methods such as bagging, boosting, and stacking are employed, leveraging the collective advantages of multiple models to generate more robust predictions. Comprehensive experimental analysis and comparisons reveal that ensemble learning significantly improves predictive accuracy and uncovers the intricate relationships between variables influencing marathon outcomes. This research contributes meaningfully to the evolving domain of sports analytics, providing valuable insights and tools for athletes, coaches, and sports scientists aiming to optimize training regimens and race strategies.