Precise measurement of muscle mass and fat distribution could revolutionize how clinicians diagnose and monitor metabolic diseases, sarcopenia, and age-related muscle loss. Current manual analysis of MRI scans is time-intensive and prone to human error, creating bottlenecks in both research and clinical care. A breakthrough AI system now demonstrates the potential to automate this critical assessment with remarkable precision. Researchers developed a sophisticated deep learning pipeline using 3D Attention-Res-V-Net architecture to automatically analyze muscle and fat tissue from MRI scans of the lower legs. Testing on 25 participants from the Asian Indian Prediabetes Study, the system achieved 92% accuracy for thigh muscle segmentation and 87% for calf muscles, with subcutaneous fat measurement reaching 95% accuracy. The AI successfully identified and quantified 22 individual muscles across thigh and calf regions, while also measuring intermuscular and intramuscular fat deposits. This technological advancement addresses a significant gap in preventive medicine and longevity research. Manual MRI analysis currently requires specialized expertise and hours of work per scan, limiting widespread adoption for muscle health monitoring. The AI framework could enable routine muscle mass tracking during routine medical imaging, potentially catching sarcopenia or metabolic dysfunction years earlier. However, the study's small sample size and focus on one ethnic population limits generalizability. The 17-59% error rates for intramuscular fat measurement also suggest refinement is needed. Still, this represents meaningful progress toward automated body composition analysis that could transform how we monitor muscle health throughout aging.