The leading cause of deaths worldwide are cardiac-related illnesses. In 2019, approximately 32% of deaths worldwide were due to cardiovascular disease, and more than 75% of these occur in low- and middle-income countries. One of the main contributing factors to this is the patient not receiving the appropriate medications on time. This issue arises as a doctor must periodically examine more than 100 biological and chemical indicators of the patient. The goal of our work is to provide doctors with a decision support system that can recommend the appropriate drugs with practically applicable accuracy (80–95%). The computational models built were of two types—artificial neural network (ANN) and support vector classifier (SVC). In contrast to the ANN, the SVC had far little complexity. The ANN was tweaked and tuned according to the drug under study as each and every drug has its own complexity when it comes to prognosis. The general architecture included six hidden layers, of which four were ReLu + L2 (Regularized) and two were dropout layers (20% dropout) to deal with overfitting. The models were trained on a dataset of 117 attributes and were able to prognose 15 different heart-related drugs with high precision.