Detecting software defects is a critical task that requires efficient and accurate methods. This research focuses on exploring the use of Adaboost classifier tuned by elk heard optimization metaheuristics to detect software defects more precisely and more effectively than existing techniques. By leveraging the inherent capabilities of the Adaboost model, for example, the capacity to understand intricate patterns and relationships within software code automatically, this study aims to identify subtle defects that might otherwise go unnoticed by traditional detection methods. This research offers a comprehensive exploration of software defect detection using Adaboost, covering both technical and practical aspects. The proposed tuned Adaboost model shows promising accuracy and adaptability, as valuable tool for modern software development practices. As industries increasingly recognize the importance of proactive defect detection, this research provides a road map for the effective integration of machine learning techniques, contributing to the ongoing evolution of software engineering practices.