The majority of deaths worldwide are caused by cardiovascular illnesses or CVDs. In developing nations like India, the largest obstacles to early identification of CVDs are a lack of doctors, an absence of authorized hospitals and diagnostic laboratories in rural regions, and high medical costs. The absence of infrastructure and the high cost of angiography tests make them impractical for diagnosing CVDs in rural locations. These factors cause CVDs to go undiagnosed, which ultimately raises death rates. Developing an affordable and user-friendly screening tool is essential for the early detection of cardiovascular diseases. In a tertiary hospital in India, this study used the adaptive sliding window method to identify the most important noninvasive clinical attributes for developing the CVD prediction system. To create prediction models, different combinations of clinical characteristics and four classification methods—support vector machines, random forests, k-nearest neighbor, and logistic regression—were used. After assessing the prediction models’ sensitivity, specificity, and accuracy, twelve important clinical features were found. A machine learning (ML) prediction system that was created with these important clinical characteristics achieved 93.8% accuracy, 92.8% sensitivity, and 94.6% specificity. This system was set up in the cloud to provide easy internet connectivity. Due to its affordability, effectiveness, and ease of use, the machine learning model developed in this work is effective in the early identification of cardiovascular diseases (CVDs) in rural areas.