The purpose of this study is to develop a linear regression model that predicts the likelihood of budget overruns in engineering projects based on a variety of factors including project complexity employee skill level safety incidents risk of regulatory compliance equipment reliability and environmental risks. A dataset comprising 177 projects describing 8 related variables and was used to train the model and its performance was assessed using metrics such as Mean Squared Error (MSE) and R-squared (R2). The models R2 value of 0. 0868 indicated that it explains only 8.68% of the variance in budget overruns while the resulting MSE of 0. 0825 indicated moderate prediction error. The low R2 suggests that the model is not very good at forecasting budget risks. More sophisticated machine learning models and feature engineering are advised to boost performance. Even with its present drawbacks the model serves as a foundation for future optimization in risk assessment and resource allocation within engineering organizations and offers a first tool for comprehending the major factors influencing budget overruns.