The transition from high school to higher education marks a serious stage in an individual's academic journey, where selecting the most suitable career path assumes thoughtful implication. The plenty of choices following high school completion necessitates a robust framework for guiding students toward optimal course selections. This paper introduces an innovative approach, the Best Course Selection System (BCSS), designed to facilitate informed decision-making leveraging machine learning methodologies. It incorporate essential criteria such as personal interests, employment prospects, eligibility criteria, affordability, duration of study, and course suitability. The BCSS integrates classifiers like Decision Tree(DT), AdaBoost, Support Vector Machine (SVM), Artificial Neural Network (ANN), etc. Three major streams—Arts, Commerce, and distinct Science streams—are considered for comprehensive analysis. Comparative evaluations based on Accuracy, Confusion Matrix, Precision, Recall, and F1-Score metrics highlight the superior performance of Support Vector Machine, Artificial Neural Network, and Decision Tree classifiers over AdaBoost. The proposed BCSS, trained and tested on a specific database, demonstrates an impressive accuracy rate of approximately 98% in predicting the most favorable course options. This system promises to serve as a tool for students navigating the complex landscape of post-secondary education choices, aiding in informed decision-making for a successful academic path.