According to the World Health Organization (WHO), Heart Disease (HD) ranks among the leading causes of death globally. Predicting HD is not an easy task, yet it is critical to prevent its further progression and reduce mortality rates. The paper presents a strong system for predicting HD using Deep Neural Networks (DNNs), analyzing data, and improving the accuracy of predictions. The system integrates four machine learning models, which include Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbour (KNN), and five deep learning models, which include Long–Short Term Memory (LSTM), Convolutional Neural Network (CNN), Multi-layer Perceptron (MLP), Artificial Neural Network (ANN), and Hybrid CNN-LSTM. These models were tested on two HD datasets. Comparative analysis was performed on different algorithms using various evaluation parameters.