This review investigates into the scenery of Indian Classical Raga Identification employing various Machine Learning and Deep Learning Techniques. Indian Classical music, with its rich heritage and complex melodic structures, poses a unique challenge for computational analysis. The paper surveys the advancements in leveraging these computational methodologies to decode and classify Ragas, aiming to bridge the gap between traditional musicology and modern technological innovations. The review highlights the efficacy of Machine Learning and Deep Learning algorithms in recognizing patterns within the complex nuances of Ragas. It identifies the progress made in automated systems designed to understand and classify Ragas while emphasizing the challenges in capturing the subtleties, improvisations, and cultural context inherent in Indian Classical music. Moreover, the review highlights the need for collaborative efforts between domain experts, musicians, and technologists to refine algorithms and datasets. It suggests future research directions to enhance the robustness, accuracy, and cultural context sensitivity of Raga identification systems. In conclusion, the review acknowledges the potential of Machine Learning and Deep Learning Techniques in Indian Classical Raga Identification while emphasizing the necessity for continued interdisciplinary collaboration, dataset enrichment, and algorithmic advancements to develop more detailed, culturally relevant, and accurate systems for preserving and comprehending this cherished musical tradition.