Career recommendation systems play a crucial role in shaping a student’s successful professional life. In today’s dynamic environment, many students often face challenges in identifying a suitable career path. Traditional approaches to career guidance include group counselling, teacher guidance, career fairs, or advice from parents and family. While these approaches are valuable, they may not always align with students’ individual skills, interests, and goals. In the current Indian educational landscape, higher education institutes are now adapting guidelines suggested by NEP 2020 to promote flexible, skill-based, and personalized learning. While theoretical frameworks of NEP 2020 have been introduced, their practical application in AI and ML-based career recommendation systems remains limited. In the current study, analysis of research work conducted between 2015 and 2025 from well-known databases such as Google Scholar, IEEE, Springer, ACM, and Scopus is reviewed in detail. Various AI & ML techniques and approaches are analyzed to understand their limitations, influencing factors, gaps in existing systems, and possible future improvements. Further, the study analyzes various approaches and determines research gaps to provide students more effective personalized career recommendations that align with the National Education Policy (NEP 2020) in an Indian context. In the education sector, Digital Twin technology has created new opportunities to improve career recommendation systems by simulating alternate career paths and their potential outcomes. The research aims to analyze the utility and applicability of Digital Twin technology in career recommendation systems.