<p>Heart disease remains one of the major causes of the high death rate across the world due to delayed detection and improper classification. The Machine Learning (ML) and Deep Learning (DL) methods introduced in the research article on the Heart Disease (HD) classification seem to occur with several drawbacks, such as misclassification, similar morphological features, improper training data, and the existing methods found computational complexity, noisy outcome, and inefficient feature extraction process. The classification of HD is performed with the modified Mixed Attention Mechanism based on Deep Bidirectional Long short-term memory (M2AM-Deep BiLSTM), which performs accurate detection and classification in the multiclass of HD. The model utilized the Band Pass filter (BPF) to preprocess the obtained signal input, which neglects the signal’s error or noise. The segmentation is carried out with the wavelet transformations. The performance of the multiclass heart disease classification research using M2AM-Deep BiLSTM achieves an accuracy of 93.82%, a precision of 96.20%, and a recall of 94.34%.</p>