CVDs are still a problem all over the world, so a new way to find and treat them early has become very urgent. This study tries to investigate whether deep learning (DL) could help make it easy to forecast heart disease risk using key health markers. From this perspective, the proposed method creates a paradigm shift in mainstream predictive modeling in healthcare by leveraging CNNs, which have the unique ability to discern hidden interactions and subtle patterns among health markers like reading of the level of cholesterol levels, blood pressure (bp), and also lifestyle factors. For such modern neural network architectures are explored. The entrenching layers convert the categorical input data into numeric form, convolutional layers extract spatial features, and dense layers represent complex interactions and estimate CVD risk. Through dropout and batch normalization, regularization, as well as hyperparameter tuning, the models are given higher capabilities in terms of generalization and performance. This success means the model could be an aid for physicians in the prevention and treatment of CVD. The paper also goes on to emphasize the necessity for explainable DL models and discusses ethical issues to address.