Heart disease continues to be one of the leading causes of demise around the world, emphasizing the urgent need for effective early detection mechanisms. Normalized Clinical Feature Neural Net (NCF-NN), a neural network-based technique designed to categorize patients based on the likelihood of cardiovascular issues utilizing 13 clinical characteristics, is proposed in this research. The architecture involves two concealed layers with ReLU activation and L2 regularization, optimized using stochastic gradient descent and binary cross-entropy loss. Leveraging a dataset of 13 standardized clinical attributes extraction, the model attained a prediction accuracy of 98%, an AUC of 0.99, and consistently robust outcomes across other evaluation metrics. These conclusions underscore the model's potential as a practical diagnostic support tool in clinical environments, offering dependable risk prediction and contributing to more informed and proactive cardiovascular care. Separately, some patients exhibited multiple risk factors increasing the complexity of analysis, while others presented with only one or two characteristics highlighting the variance in presentations. The model successfully classified cases along this spectrum demonstrating its ability to evaluate diverse patient profiles.