With a major worldwide source of morbidity and death, cardiovascular disease (CVD) emphasizes the need of accurate and quick prognostic approaches. In predicting CVD based on demographic, clinical, and lifestyle variables, this study evaluates the efficacy of a range of machine learning (ML) models, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and a Hybrid Model that combines DT and LR. With best accuracy, precision, recall, F1-score, and ROC-AUC, the Hybrid Model was the most successful. This was so because its ability to record in the data both linear and non-linear relationships. This approach uses the interpretability of the DT to underline the major predictors and the classification power of LR, therefore guaranteeing predictive accuracy and openness. By means of insight into important risk factors and efficient identification of high-risk people, hybrid ML models show to be rather helpful in clinical decision-making. Nevertheless, as shown by the restrictions related to data diversity and longitudinal dynamics, further study including various and temporal datasets could help to increase the generalizability and robustness of the model. Emphasizing the need of balanced model performance and interpretability in the healthcare industry, this paper adds to the growing corpus of data supporting the usage of ML. e context of clinical adoption.