Objectives: To identify a new method for early detection and classification of Heart Diseases (HD); to improve the accuracy of the results by employing I-FCMML (Improved Fuzzy C-Means Machine Learning) and 2L-C (Two-Level Classifier) models along with the I-GA (Improved Genetic Algorithm) methods. Methods: The I-FCMML algorithm is utilized for feature selection and extraction. Machine learning techniques such as Ensembled Random Forest method (ERFM) and Robust Gradient Boost Method (RGBT) are employed to predict the likelihood of HD and 2L-C, I-GA is used to classify and detect the HD at a premature stage based on features like age, gender, blood pressure, etc. I-FCMML with 2L-C & I-GA extracts all the features from the dataset (Cleveland HD dataset from the Cleveland Clinic Foundation, Ohio, USA) which includes 303 observations, 14 features and selects the suitable function to perform disease classification and detection with high accuracy. To evaluate the performance of the proposed method, MATLAB is used for implementation. The results are compared with existing algorithms such as 3P-ANN, ANN-FAHP, ADWFS, EDSS, and FE-PCA. Findings: Early HD detection and classification is achieved with 96.02% accuracy, 95.80% sensitivity, 94.76% specificity, 95% precision, 94% recall, 0.90 True Positive, 0.87 True Negative, and 94.13% F-Score to detect and classify the HD in a robust manner, which is comparatively high than the existing methods. Novelty: According to the findings of the comprehensive study, the proposed new method I-FCMML with 2L-C & I-GA has the potential to provide accurate and competent detection and classification of HD at an early stage, which could help for timely treatment and management of HD patients, and it also outperforms the existing methods such as 3P-ANN, ANN-FAHP, ADWFS, EDSS, and FE-PCA.