In this research, an improved automated medical prediction system, namely, the Neoplasm Medical (MRI) Image Classification System ( NMICS), is proposed. The proposed (NMICS) system aimed to robotically identify the test medical (MRI) image, which s grouped into the neoplasm (Tumor) or non-neoplasm (non-tumor) group, respectively, using machine learning techniques. The proposed (NMICS) system is divided into two stages, namely, the Train Medical (MRI) Image Model (TMIM) and Medical Image Predication Stage (MIPS), respectively. In the TMIM stage, the NMICS system is performing various distinct operations including 1) improving input medical (MRI) image data set quality and consistency through standard arithmetic operations, 2) extracting the specific features (edge) from every individual medical image in the input MRI image data set using the CNN method and 3) separating the feature vector set of the input MRI image data set into two distinct clusters, namely, Tumor and Non-Tumor, respectively, using the unsupervised k-means clustering technique. In the MIPS stage, the NMICS system is performing the same types of operations over the test medical image samples, which are followed in the TMIM stage excluding training operation. Next, the NMICS system maps and classifies the feature vector of the test medical image sample with trained medical image data set clusters using a KNN classifier. The investigation results show that the NMICS system is well suited to diagnosis whether the given MRI image is grouped into the neoplasm category or non-neoplasm group.