This study examines the effectiveness of Neural Network (NN) and Artificial Neural Network (ANN) models in accurately predicting the occurrence of coronary artery disease (CAD) by utilising the Cleveland Heart Disease dataset. The goal is to evaluate the efficacy of these models in binary classification, assisting in the prompt identification and intervention for CAD. The methodology consists of thorough data collection, preprocessing, Exploratory Data Analysis (EDA), model training, and evaluation. Preprocessing involves addressing missing values and transforming data into a numerical format to maintain data integrity. Exploratory Data Analysis (EDA) tools, such as violin plots, count plots, histograms, and heatmaps, provide valuable insights into the distributions, patterns, and relationships present in the data. The dataset is divided into training and testing sets using stratified splitting to precisely assess the performance of the model. The results indicate that the Artificial Neural Network (ANN) model performs better than the Neural Network (NN) model, showing a smaller loss (0.33 compared to 0.35) and higher accuracy (87.60% compared to 86.36%). The exceptional performance of the ANN can be due to its sophisticated structure, which includes hidden layers that effectively capture complex data patterns. The results emphasise the potential of deep learning methods, namely artificial neural network (ANN) models, in reliably predicting the existence of coronary artery disease (CAD). This has significant implications for early identification and management in the field of cardiovascular medicine and beyond.