Cardiovascular disease has a way of slipping into everyday life almost unnoticed until it suddenly becomes serious, which is why early prediction has turned into such a central concern in healthcare. The difficulty is not only that the condition is widespread, but that it grows out of a messy mix of biological, behavioural, and lifestyle factors that rarely line up in a simple, linear way. Traditional screening tools try to tame this complexity with fixed rules and risk scores, yet they often miss faint, interacting patterns in large datasets. Machine learning models, by contrast, learn directly from patient data and can sometimes pick up relationships that are too subtle or too tangled for conventional methods to capture. In this project, the focus is on whether a Radial Basis Function Neural Network can meaningfully support cardiovascular risk prediction. RBF networks sit in an interesting middle space: they are flexible enough to approximate complex decision boundaries, but they remain more interpretable than many deep architectures that are used in clinical prediction tasks. Using a publicly available dataset with common clinical variables such as age, blood pressure, cholesterol, glucose, and lifestyle indicators like smoking, alcohol use, and physical activity, the model was trained to classify individuals as having cardiovascular disease or not. Working through preprocessing, tuning the RBF parameters, and evaluating the predictions highlighted both the promise and the limits of this relatively simple architecture. Given the stakes in healthcare, such models need to be treated with caution, but they still offer a useful glimpse of how early detection might be supported in practice rather than replaced outright.