This research investigates the use of ML and deep DL algorithms to predict heart disease, addressing the important need for reliable early identification in order to lessen its huge global health effect. The study uses preprocessing approaches to manage data completeness and normalization on a variety of datasets, including demographic information, clinical records, and diagnostic test results. These datasets are subjected to algorithms such as logistic regression, decision trees, ensemble approaches, CNNs, and RNNs, with the goal of identifying patterns that indicate heart disease risk. Results show significant gains in predicting accuracy compared to traditional approaches, with ML models reaching area under the curve scores more than 0.85 and DL models outperforming with AUC ratings greater than 0.90. This research underscores the transformative potential of ML and DL in advancing early diagnosis and personalized treatment strategies for heart disease, thereby enhancing healthcare outcomes on a broader scale.