In the realm of healthcare, the integration of artificial intelligence (AI) has revolutionized medical imaging analysis [1, 2, 3]. This research paper presents a comprehensive medical imaging platform powered by artificial intelligence (AI) techniques for automated image analysis. The platform incorporates specialized AI models for image classification using ResNet50 [4], object detection using YOLOv5l [7] and segmentation using Res50-U-Net [10]. The automated pipeline seamlessly categorizes incoming medical images, directing them to appropriate analysis modules - MRI and X-ray images trigger object detection to identify abnormalities; MRI brain images undergo additional tumor segmentation. Through three pivotal phases focused on classification, detection and segmentation, this research demonstrates an effective framework to harness AI for enhanced efficiency and precision in medical image evaluation, paving the pathway towards improved clinical diagnostics and patient care. The platform's automated workflow for progressive image analysis sets it apart from existing systems. Outcomes indicate AI's immense potential to transform medical imaging, assisting clinicians through actionable insights while mitigating subjectivity and variability in manual evaluation.