Mango (Mangifera indica) is of great significant fruit crop which grows in different agro-ecologies in the world. Mangoes are good sources of vitamins and minerals. However, nowadays its productivity is very limited since it is attacked by different diseases and pests. Thus to increase the mango fruit quality and productivity, it is crucial and feasible to detect diseases and insect pests at the early stage. In this study, we have designed and developed mango leaf disease identification mechanism using machine learning (ML) technique. Healthy and diseased mango leaf images were captured manually from main production areas in Amhara Region such that Weramit fruit and vegetable research and training sub-centre, and Bahir Dar city for the identification method. As an implementation tool, Python on an anaconda Spyder working environment, and Google Collaboratory were used. To enhance the dataset different preprocessing techniques (i.e. image resizing, histogram adjustment, noise removal, and image augmentation) using the OpenCV library were applied. To enhance the classification performance and to achieve the objective of this study different segmentation techniques such as k means, Mask R-CNN, and combined were used. Besides, after the pre-processing and segmentation steps, features of mango leaf images were extracted using CNN to get the relevant features. Then the classification model was built using CNN, SVM, and CNN-SVM classifiers on the extracted features of mango leaf images. For these classification models three different activation functions such that Tanh, Relu, and Leaky Relu were applied to achieve better classification accuracy. From the experiment, we noticed that these classifiers using segmented images and Leaky Relu activation function were achieved a significant classification performance with an accuracy of CNN 97.62%, SVM 98.01%, and CNN-SVM 99.78% respectively.