Cardiovascular disease (CVD) is the leading cause of global mortality, claiming an estimated 1 in 3 lives each year. An epidemic of worldwide concern, its geographical spread highlights the imperative need for protective intervention against this infectious disease. Early detection, prevention, and precision prognosis are new distinguished management of this deadly disease. In this study, several machine learning and deep learning methods are used to assess their performance. It specifically discusses the implementation of Feedforward Neural Network (FNN) with multilayer perceptron using dropout regularization. Various encouraging results are obtained and cross-verified with accuracy and other performance metrics. Using data from UCI and focusing on the top 14 most important factors, this research seeks to achieve better prediction accuracy. More impressive results were shown in the findings, with a detection accuracy of 98.5% being achieved by FNN, surpassing that of conventional neural networks. Our study aims to enable preventive healthcare by leveraging state-of-the-art deep learning approaches like FNNs, which can empower timely intervention and enhance patient prognosis. By combining cutting-edge technologies with large-scale datasets, this research represents a significant step towards reducing the global burden of CVD, providing a platform for more effective screening strategies and better predictive tools to improve the prevention and treatment of CVD.