Aim: This following research compares the Novel Naive Bayes Algorithm and the K-Nearest Neighbor Algorithm for stock price prediction optimization in order to enhance the Accuracy of real-time stock exchange. Materials and Methods: To optimize the pH, the Novel Naive Bayes Algorithm (N=10) and K-Nearest Neighbor (N=10) are simulated by adjusting the Novel Naive Bayes parameters and K-Nearest Neighbor parameters. Gpower 80 percent is utilized to compute sample size for two groups, and 20 samples are investigated in this research. Results: Utilizing SPSS Software, an independent sample size is used to evaluate the accuracy rate. Although K-Nearest Neighbor generates 46.50 percent accuracy, Naive Bayes produces 82.19 percent accuracy. The difference in statistical significance between Naive Bayes and KNN was discovered to be 0.016 (p<0.05). Conclusion: In terms of accuracy, the Naive Bayes algorithm outperforms the K-Nearest Neighbor algorithm.