Heart disease analysis, prediction, and early detection are vital challenges within the healthcare domain. The availability of expensive therapies and medical interventions emphasizes the significance of anticipating heart diseases before they reach critical stages. By performing a thorough examination into the prediction of heart illness using various machine learning algorithms, such as XGBoost, Naive Bayes, SVM, Logistic Regression, Random Forest, and LSTM, etc., this work intends to contribute to global healthcare. The comparison of various machine learning algorithm’s predictive ability, as measured by their accuracy, recall, precision, and F1-score is the study’s primary objective. We conduct this to find the best and most reliable models for predicting heart disease and predict that XGBoost is performing for all the measures. The results of this study may enable healthcare professionals and decision-makers to choose early intervention strategies that will ultimately enhance patient outcomes.