This research investigates a hybrid model for stock Market prediction that combines a K Nearest Neighbors (KNN) approach with a Probabilistic strategy. The assumptions suggested by distance function are one of the fundamental challenges with KNN classification. The assumptions are based on the test instances closest Neighbors, which are at the centroid of the data points. This method eliminates non centric data points from equation, which can be statically important in predicting stock price movements. To do this , an upgraded model must be built that combines KNN with a probabilistic technique that computes probability for target instances using both centric and non-centric data points . Baye’s theorem is used to create the integrated probabilistic technique. KNN , Naïve Bayes , one Rule (oneR) , Zero Rule were used to evaluate the proposed hybrid KNN Probabilistic model against conventional Classifiers ( ZeroR) .Keywords – Stock Price Prediction, K-Nearest Neighbors, Bayes’ Theorem, Naïve Bayes, Probabilistic Method.