Modern technology has contributed to modern society, and software developers play a significant part in developing and maintaining this technological world. In this complex field, accurate salary prediction is crucial for job seekers and recruiters. By applying machine learning techniques, we predict software engineering salaries effectively. Leveraging a dataset of historical salary information and various relevant features such as years of experience, education level, and geographical location, we employ regression models to estimate future or current software engineers’ salaries. The methodology integrates data preprocessing, feature selection, model training, and evaluation to construct a robust prediction framework. We explore several machine learning algorithms, including linear regression, decision trees, random forests, support vector machines, XGBoost, and others, with extensive performance comparisons. Our research demonstrates the effectiveness of machine learning models in accurately forecasting software engineers’ salaries, achieving a highest accuracy of 76% and the lowest root mean squared error of 9.59%. This model empowers job seekers and employers to make more informed decisions regarding salary expectations. This research contributes to the field of software engineering by offering a valuable tool for salary negotiation and career planning, enhancing transparency and efficiency in the software job market.