Heart disease remains a major global health concern and one of the leading causes of mortality, emphasizing the importance of early detection and reliable risk assessment. This research investigates the effectiveness of Logistic Regression (LR) in predicting heart disease risk using the Heart Disease Statlog dataset. Various clinical and demographic parameters including age, cholesterol levels, exercise-induced angina, and ST depression were examined to build a predictive model. The dataset was subjected to preprocessing, feature selection, and exploratory data analysis (EDA) before model training. Evaluation metrics such as accuracy (92.59%), precision (94.7%), recall (85.7%), and AUC-ROC (0.95) demonstrated strong model performance. The study underscores the interpretability of LR for medical diagnosis while recognizing its limitations in modeling complex non-linear patterns. Future studies may enhance prediction accuracy by employing advanced machine learning techniques and integrating real-time health monitoring systems. Overall, the findings support improvements in early disease detection, healthcare decision-making, and actuarial risk analysis in the health insurance sector.