Early Prediction of Heart disease is a major concern now a days because of its mortality rate in the global society, which requires effective ways of detection. The increased availability of clinical and lifestyle information offers the possibility to implement the machine learning methods to help in the adequate diagnosis of a patient. We put forward a Hybrid Random Forest-Logistic Regression (HRFLM) algorithm that uses Random Forest for selection and Logistic regression for classification. A comparative analysis of different models along with the proposed method for heart disease prediction are monitored with respect to the feature-rich dataset of demographics, clinical, and behavioral factors. The Support Vector Machine, Decision Tree, Random Forest, and HRFLM are the tested ones. Upon data processing and cleaning, the models are evaluated based on different performance metrics. The experiments prove that the HRFLM Model works more effectively than regular machine learning models. The hybrid method had a high precision and a high reduction in false negatives of the tool, which proved to be an effective tool of clinical decision support in the early detection of heart diseases.