Brain Cancer is quite possibly the most driving reason for death in recent years. Appropriate diagnosis of the cancer type empowers the specialists to make the right choice of treatment, decision and to save the patient's life. It goes no saying the importance of a computer-aided diagnosis system with image processing that can classify the tumor types correctly. In this paper, an enhanced approach has been proposed, that can classify brain tumor types from Magnetic Resonance Images (MRI) using deep learning and an ensemble of Machine Learning Algorithms. The system named BCM-VEMT can classify among four different classes that consist of three categories of Brain Cancers (Glioma, Meningioma, and Pituitary) and Non-Cancerous which means Normal type. A Convolutional Neural Network is developed to extract deep features from the MRI images. Then these extracted deep features are fed into multi-class ML classifiers to classify among these cancer types. Finally, a weighted average ensemble of classifiers is used to achieve better performance by combining the results of each ML classifier. The dataset of the system has a total of 3787 MRI images of four classes. BCM-VEMT has achieved better performance with 97.90% accuracy for the Glioma class, 98.94% accuracy for the Meningioma class, 98.00% accuracy for the Normal class, 98.92% accuracy for the Pituitary class, and overall accuracy of 98.42%. BCM-VEMT can have a great significance in classifying Brain Cancer types.