Cardiovascular disease is one of the major causes of death today also. Machine learning can play a major role in early prediction of disease. The main goal of this work is to combine Particle Swarm Optimization (PSO) techniques with Decision Tree algorithms to create an accurate and efficient prediction model for cardiovascular disease. Because the study makes use of a well-known dataset from Kaggle, the data used for model training and validation is guaranteed to be reliable and relevant. The study finds the most important characteristics by using Mutual Information for feature selection, which lowers dimensionality and boosts model performance. Using these chosen features, a Decision Tree model is created and trained, with an initial accuracy of 86.38%. When Particle Swarm Optimization is combined with the Decision Tree model, accuracy increases dramatically, reaching 92.38%. The enhanced model provides a strong tool for medical practitioners, supporting early and precise cardiovascular disease prediction—an essential step toward prompt intervention and therapy.