Intelligent prediction of the flexural strength of reinforced concrete beams remains a challenging task due to variation in accuracy and precision of machine learning algorithms. It also becomes harder for users to assess and select best model, since several machine learning models has their own limitation and advantages with different circumstances. To overcome this problem this study aims to evaluate and compare the performance of various machine learning algorithms in terms of their accuracy and efficiency. Linear Regression, Decision Tree, Support Vector Machine, K-Nearest Neighbors, Random Forest, Adaptive Boosting, Gradient Boosting, and Extreme Gradient Boosting models were evaluated. Hyperparameters of each model were optimized using Grid Search cross validation with Mean Squared Error used as the Performance Index. The predictive efficiency of each model was rigorously evaluated through four distinct statistical performance measures. The results of the analysis revealed that the Linear Regression model encountered issues of underfitting, while the Decision Tree model demonstrated signs of overfitting and constrained generalization capabilities. Additionally, the Adaptive Boosting model exhibited a minor overfitting concern. Moreover, the Support Vector Machine, Random Forest, and Adaptive Boosting models yielded comparable levels of accuracy. In contrast, the proposed Extreme Gradient Boosting model achieved superior performance characterized by exceptional generalization capabilities, as evidenced by its minimal mean absolute error of 2.08 kN-m, a root mean squared error of 3.09 kN-m, and the highest coefficient of determination of 98.50% on the test data. Key Words: Machine Learning, Artificial Intelligence, Reinforced Concrete Beam