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Selecting the most effective welding settings impacts mechanical strength and weld quality, with parameters like current, voltage, and speed playing pivotal roles. The mechanical part encompasses material properties, welding process variables, and structural integrity, all contributing to the overall weld quality and strength. By integrating these mechanical factors with predictive modelling, a comprehensive understanding of weld performance can be achieved, enabling optimized welding settings and enhanced weld quality assurance. This study assesses and compares machine learning algorithms such as a random tree, random forest, and C4.5 to determine their predictive capability regarding the tensile strength in Monel 400 Weldments. By utilizing a dataset comprising 32 instances with attributes like Current, Voltage, and Speed, models were developed and assessed using K-Fold cross-validation. Among these algorithms, the random tree models emerge as the most proficient in accurately predicting the tensile strength for Monel 400 Weldments through classification ML techniques. Similarly, regression algorithms have been deployed to assess the dataset by varying the train-test split ratio and gradient boosting, which exhibited superior performance with a higher R ^2 value of 0.99. Both random tree and Gradient boosting algorithms have commonly been recommended, with current being the most influential factor affecting tensile strength.