Effort estimation in software development projects remains a critical challenge due to its inherent complexity and uncertainty. Traditional estimation methods often suffer from inaccuracies, leading to project delays, budget overruns, and suboptimal resource allocation. To address these issues, this research proposes the application of Extreme Learning Machine (ELM), a machine learning algorithm known for its simplicity, efficiency, and effectiveness in handling nonlinear regression tasks. This study leverages historical project data comprising various features such as project size, complexity, team expertise, and development environment to train the ELM model. By employing a large dataset collected from diverse software projects, the model is trained to predict the effort required for future projects accurately. The performance of the ELM approach is evaluated against other commonly used estimation techniques, including linear regression and support vector regression, using metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results demonstrate the superior accuracy and efficiency of the ELM-based effort estimation approach compared to traditional methods. The proposed model exhibits robustness in handling complex software projects with varying characteristics, thereby providing valuable insights for project managers and stakeholders to make informed decisions regarding resource allocation, scheduling, and risk management. Additionally, the simplicity and computational efficiency of ELM make it suitable for real-time estimation tasks, enhancing its practical applicability in software development environments.