In today's digital era, the proliferation of image processing tools has made image forgery detection a critical challenge. Malicious actors exploit these tools to manipulate images, spreading misinformation and misleading society. Existing tampering detection methods struggle with detecting complex transformations such as copy-rotate-move forgeries, often facing limitations in computational efficiency, robustness, and accuracy. Many approaches rely on traditional feature extraction techniques that fail under severe transformations or require extensive processing time. To address these shortcomings, we propose a novel and computationally efficient algorithm that integrates Radon Transform with Biogeography-Based Optimization (BBO) for enhanced copy-rotate-move forgery detection. Unlike conventional optimization techniques, BBO effectively enhances feature selection and matching, improving detection robustness against rotation and scale variations. The proposed algorithm has been rigorously evaluated on multiple benchmark datasets, demonstrating superior performance in terms of F1-score, recall, and accuracy compared to existing state-of-the-art methods. The results affirm that our approach significantly improves forgery localization while maintaining computational efficiency, making it a promising solution for real-world digital forensics applications.