The global prevalence of heart disease indicates a major public health issue. It causes shortness of breath, weakness, and swollen ankles. Early heart disease diagnosis is difficult with current approaches. Hence, a better heart disease detection tool is needed. Treatment requires more than just diagnosis. Risk classification is critical for accurate diagnosis and treatment. In this analysis, a novel cardiovascular disease (CVD) detection paradigm using machine learning (ML) and risk classification based on a weighted fuzzy system is proposed. The system is developed based on ML algorithms such as artificial neural network (ANN) and Long Short-Term Memory (LSTM) and uses standard feature selection techniques knowns as Principal Component Analysis (PCA). Furthermore, the cross-validation method has been used for learning the best practices of model assessment and for hyperparameter tuning. The accuracy-based performance measuring metrics are used for the assessment of the performances of the classifiers. Finally, the outcomes revealed that the proposed model achieved an accuracy of 94.01% which is higher than another conventional model developed in this domain. Additionally, the proposed system can easily be implemented in healthcare for the identification of heart disease