Machine learning is increasingly important in many facets of our lives as technology develops, including forecasting weather, figuring out social media trends, and predicting prices on the world market. This significance invoked the demand for efficient predicting models that can easily handle complex data and provide maximum accurate results. XGBoost and Random Forest are upgradable ensemble techniques used to solve regression and classification problems that have evolved and proved to be dependable machine learning challenge solvers. In this research paper, we comprehensively analyze and compare these two prominent machine learning algorithms. The first half of the research includes a relevant overview of both technique's significance and the evolution of both algorithms. The latter part of this study involves a meticulous comparative analysis between Random Forest and XGBoost, scrutinizing facets such as time complexity, precision, and reliability. We examine their distinctive approaches to handling regression and classification problems while closely examining their subtle handling of training and testing datasets. A thorough quantitative evaluation using a variety of performance metrics, such as the F1-score, Recall, Precision, Mean Squared Error, and others, concludes this discussion.