High Entropy Alloys (HEAs) have emerged as a novel paradigm in alloy design due to their superior material properties compared to the normal alloys. However, designing the HEAs with multiple metallic elements and their molar fractions is a challenging task. To resolve this issue, this project proposes a modern approach for designing new HEAs with high yield strength by utilizing machine learning techniques. The study outlines a comprehensive process for collecting and pre processing dataset from online resources, and explores the application of five different machine learning models, two hyperparameter tuning methods, and two optimization algorithms to design HEAs. The effectiveness of each approach is evaluated based on their ability to predict the yield strength of HEAs and determine the required molar fractions of metallic elements to obtain high yield strength. The findings of this study suggest that the proposed method can serve as a valuable reference in the experimental design process of HEAs.