Accurate price prediction is essential for investors, analysts, and policymakers to make informed decisions in the ever-changing world of stock markets. This review paper investigates the relationship between predictive modeling and stock market analysis by utilizing the functions of algorithms. This review assesses the performance of 13 algorithms in stock price forecasting by looking at 15 research articles released between 2018 and 2023. The algorithms that are used in this context include AdaBoost, Artificial Neural Network (ANN), Decision Tree, Gradient Boosting, K-Nearest Neighbors (KNN), Logistic Regression, Naive Bayes, Random Forest, Recurrent Neural Network (RNN), Support Vector Classifier (SVC), the Support Vector Machine (SVM), and XG Boost. This review offers a thorough overview of the advantages, disadvantages, and corresponding effectiveness of multiple algorithms within the context of forecast by combining insights based on these investigations.