Heart disease is the most considerable cause of death worldwide, and so there is a need for efficient predictiontechniques. This paper presents the application of ML and DL techniques to predict heart disease. For this purpose, variousmodels such as Logistic Regression, Random Forest, Support Vector Machines, and Neural Network were trained andevaluated with an input data set consisting of 1,025 records. Normalization and encoding were applied as processing steps toenhance performance. Results indicate that the Random Forest model had the highest accuracy at 92%, followed by DeepNeural Network at 91%. This study presents that ML and DL are potentially used in aiding early diagnoses; healthcare issuescan be better addressed.Keywords: Heart Disease, Machine Learning, Deep Learning, Prediction, Healthcare