Oral cancer is a significant public health issue and is of serious concern across the globe. It becomes a genuine necessity to provide inexpensive and early-stage detection of the disease to undertake necessary precautions. The experimental study discussed in this paper recommends AIML based analysis of oral images that detect oral cancer and classify into binary and five-class classification. The experiments compare results of a few selected classifiers like KNN, CNN, Decision Tree, Random Forest, and SVM. Algorithm is selected based on the image category and probable dominant features. Image pre-processing steps are employed to eliminate noise, enhance visual quality, improve contrast and normalize data to advance classification performance. A hybrid model involving more than one classifier is implemented for claiming 8 to 10% increase in the classification accuracy. Model stability is preserved and computational complexity is maintained within acceptable range. Region growing segmentation is undertaken to identify lesion boundaries. Results show that the proposed methodology offers a scalable solution to clinical decision and support.