Surgical Tele-training has not been effectively implemented in telemedicine because of strict requirements for throughput and delay minimization during real-time high-quality video transmission. Therefore, the aim of this paper is to propose a new solution for surgical Tele-training with improved quality of real-time surgical video’s transmission, through put and minimized end-to-end delay required during the training session. The proposed system consists of an Enhanced Path Quality, Video Quality, and Latency Minimization (EPQVQaLM) algorithm to reduce network latency and improve the quality of real-time video transmission by including predicted data delivery time and packet drop probability in path quality calculation. The system also consists of cloud-based online video transcoding for economic transcoding of large-volume videos while guarantying the QoS parameter in case of additional resource requirement. The result shows that EPQVQaLM achieves improvements in PSNR by an average of 5db over the state of art solution. Furthermore, the proposed solution can reduce the processing time and end-to-end delay by an average of 32.6 ms and 33.93 ms over the state of art solution respectively. The proposed system concentrates on reducing end-to-end transmission delay and improves quality of surgical video transmission by using an EPQVQaLM technique. Meanwhile, it provides the mechanism for dropping the packets and re-routing packets to the different path before congestion becomes critical. Thus, this study provides an efficient mechanism to intelligently distribute data chunks over multiple paths.