Heart disease is becoming one of the critical diseases day by day in the current global scenario. Clinical data analysis faces huge challenges in heart disease prediction due to the increased number of cases and common symptoms across multiple diseases. Thus, this work attempts to improve the early detection of heart failure to save lives. It employs machine learning algorithms, including logistic regression, Decision Tree, Random Forest, K-nearest neighbors Algorithms, Support Vector Machine, Stochastic Gradient Decent, Multi-layer perceptron (MLP), XGBoost, Ada Boosting, Extra Tree, Gaussian Naïve Bayes, and Gradient Boosting Algorithm (GBA), to compare their performance to achieve this task. Further, this paper proposes an enhancement to the proposed hybrid Multi-layer perceptron (MLP) model with the Gradient Boosting Algorithm (GBA) by developing a novel feature set that achieves the highest possible accuracy scores. All methods have been successfully validated using the cross-validation method. The efficacy of the proposed model was evaluated by using evaluation metrics such as accuracy, precision, recall, and F1 score. The hybrid proposed model predicts early heart disease with a 98% accuracy rate, according to the results, demonstrating extraordinary accuracy. This grouping combination leads to enhanced accuracy, robust feature selection, better treatment of high-dimensional and unnecessary data, and improved simplification and interpretability. This proposed work has important scientific value in the medical field for improving cardiovascular risk assessment.