Radiology departments play a very pivotal role in the diagnosis of and care for patients. However, these departments predominantly suffer from inefficiencies such as long waiting times, heavy workloads, and high diagnostic rates. These result from fragmented scheduling, manual processes, and limited decision-support tools. This study proposes a transformative approach through the integration of LSS methodologies with advanced hardware and AI-driven software to enhance operational efficiency and diagnostic accuracy. It integrates the Lean principles on waste removal, Six Sigma to reduce the variability of processes, and AI tools for automating workflows and decision support. Key interventions will be the adoption of automated image scanners, AI-powered diagnostic assistance, and centralized workflow management systems to optimize scheduling, machine utilization, and radiologist productivity. Pilot implementation of these solutions will be done and further refined, with tracking of key metrics including, but not limited to, patient wait times, diagnostic accuracy, and radiologist throughput, to measure impact. The expected outcomes are: a 30% reduction in patient wait times, a 20% increase in diagnostic accuracy, and improved radiologist productivity by minimizing administrative burdens. The use of AI aims at smoothening workflows to achieve more patient satisfaction and minimize errors and thereby improve the quality of care as a whole.  In summary, these findings set forth a scalable model for radiology departments to drive toward improved efficiencies and outcomes, allowing for faster and more accurate diagnoses in an increasingly demanding healthcare environment. This research underlines the potential of combining LSS and AI in solving critical challenges in radiology and improving the delivery of patient care.