Background: The increasing prevalence of breast cancer underscores the need for advanced technologies to enhance early detection and improve diagnostic accuracy. Artificial Intelligence (AI) presents a promising avenue for augmenting existing medical imaging techniques to achieve more reliable and timely identification of breast cancer. Aim: Our current research aims to discover role of Artificial Intelligence in initial recognition of breast cancer by integrating machine learning algorithms with medical imaging. The primary aim is to evaluate the effectiveness of our integration in enlightening accuracy and efficiency of breast cancer diagnosis. Methods: We conducted the complete review of existing literature on AI applications in breast cancer detection and medical imaging. Subsequently, we developed and implemented machine learning algorithms, utilizing a diverse dataset of medical images to train and validate the models. The integration of these algorithms with medical imaging aimed to establish a robust framework for early detection. Results: Our findings demonstrate the successful integration of machine learning algorithms with medical imaging, resulting in a significant enhancement in accuracy of breast cancer detection. The AI-driven system exhibited a high sensitivity and specificity, surpassing traditional methods. Moreover, the efficiency of diagnosis was notably improved, leading to quicker and more precise identification of potential malignancies. Conclusion: The integration of machine learning algorithms with medical imaging holds immense potential for revolutionizing the early detection of breast cancer. Our study highlights the efficacy of Artificial Intelligence in improving diagnostic accuracy, thereby paving the way for more effective and timely interventions. The findings underscore the importance of continued research and implementation of AI technologies in field of medical imaging for enhanced healthcare outcomes.