Pancreatic carcinoma is the common term used for pancreatic cancer. The pancreas, being the most crucial organ that generates the enzymes required for the digestion process, helps to break down the complex sugar substances into smaller molecules, thus making it the most significant organ for digestion. Pancreatic cancer is rare but chances for cure is low compared to other cancer categories. Thus, early prediction helps is increasing the possibilities of cure. The successful integration of machine learning/deep learning with the medical diagnostics possess a great potential in early detection of such diseases. In this regard, the presented paper aims to demonstrate the comparative performance of multiple machine learning (classification) models including “k-nearest neighbor (KNN)”, Gradient Boosting, Light Gradient Boosting Machine (LGBM) Classifier, Random Forest and deep learning algorithms including single layer perceptron and two-layer multilayer perceptron. Amongst all the explored classification models, two-layer multilayer perceptron is observed to be superior in detecting the pancreatic carcinoma with an accuracy and loss of 97.45% and 0.019%, respectively.