Predicting salary trends in the dynamic tech industry is vital for employers and job seekers. Existing solutions often rely on traditional statistical methods, but fail to fully capture the complexity of salary variations. In this study, a novel approach is proposed using machine learning algorithms to predict tech industry salaries based on factors like experience level, job title, and company location. The methodology involves training multiple regression models, including linear regression, random forest regression, and XG Boost regression, on a comprehensive dataset of tech industry salaries. Through rigorous evaluation, the impressive results are achieved, with mean squared errors ranging from $3.25 billion to $3.27 billion. This research expands the scope of salary prediction in the tech sector and provides valuable insights for employers and job seekers. The models show high accuracy rates, with R-squared values exceeding 0.85 in all cases, indicating strong predictive power. The feature importance analysis reveals that job title and company location are the most significant factors influencing salary variations. This study opens avenues for further research in salary prediction and offers practical implications for human resource management and talent acquisition strategies.