Human body prioritizes the heart as the second most important organ after the brain. Any disruption in the heart ultimately leads to disruption of the entire body. Being the members of modern era, enormous changes are happening to us on a daily basis that impact human lives in one way or the other. A major disease among top five fatal diseases includes the heart disease which has been consuming lives worldwide. Heart disease detection has long been considered as a critical issue. Machine Learning (ML) techniques find its use in medical sciences in solving real health-related issues by early detection and treatment of various diseases. Hence in this paper Convolutional Neural Network based Instance Segmentation using Machine Technique is presented for detection of Heart Disease. This paper intends to detect heart disease using datasets through a convolutional neural network and machine learning classification models like Random Forest and Gaussian Naïve Bayes (NB). The performance evaluated based on the accuracy, precision, recall, and F1-score for each of the models.