Heart disease continues to be among the top causes of death globally and is a considerable challenge to universal healthcare systems [46]. Early and proper prediction of heart disease can lead to timely interventions by medical staff, greatly impacting patient outcomes while saving healthcare budgets. Conventional diagnostic techniques while effective tend to be time consuming and subject to human error while requiring comprehensive clinical evaluations and special expertise [6]. Advances in machine learning in recent times have created new opportunities for automating disease prediction with high accuracy and reliability. This research examines several machine learning algorithms, such as Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, K-Nearest Neighbors, Gradient Boosting, and a Neural Network model, to forecast heart disease using patient data [1, 3, 5]. In addition, a hybrid voting classifier is presented to improve prediction accuracy by combining the strengths of different models. Preprocessing is performed on the dataset using feature scaling, missing value handling, and exploratory data analysis (EDA) methodologies to maintain data quality and optimize model performance [4, 7]. Experimental outcomes demonstrate that ensemble learning approaches—particularly the hybrid voting classifier—outperform individual models in terms of accuracy and reliability. The hybrid voting classifier achieved a 90.16% accuracy rate, making it an efficient prediction tool for heart disease [2, 8]. The results of this research is highlighting potential that machine learning models have to support medical professionals in early diagnosis, ultimately contributing to better patient care and a reduction in mortality rates. Future studies may explore the integration of deep learning methods, optimized feature selection, and real-time clinical data to further enhance predictive performance [9].