Machine learning models have emerged as the most accurate prediction choice in various fields in recent year. These include healthcare, agriculture, cybersecurity, finance, etc. Now a days medical science is one of the most benefited fields from machine-learning models. If the model is appropriately trained, machine-learning techniques can diagnose many diseases early. Early diagnosis and effective health management can save many lives from severe heart disease. This research paper proposes a new machine learning model, NeNBoost, for early detection of heart disease. It uses a National Government open source dataset based on the living style of the samples. To balance the dataset, SMOTE algorithm is used, and then an Artificial Neural Network (ANN) combined with Gradient Boost is applied using the features of the neural network. Compared to other ML algorithms, the proposed model’s efficacy is estimated based on its accuracy, precision, recall, F1-Score, and AUC-ROC score. The analysis signposts that the suggested model outperformed with the maximum accuracy of 96% in comparison to other models.