This systematic literature review investigates the transformative potential of machine learning (ML) and artificial intelligence (AI) in career guidance, focusing on developing hybrid recommendation systems. The review emphasizes the notable surge in research activity in this field, with the Random Forest and Decision Tree algorithms emerging as the most often utilized because of their resilience and interpretability. The creation of competency-based frameworks, career trajectory prediction, and intelligent career systems are some of the key uses of machine learning that have been recognized. The results highlight the advantages of merging collaborative and content-based filtering techniques to produce more precise and customized career counselling tools. Along with addressing practical and ethical issues including algorithmic bias and data privacy, the paper makes recommendations for future research areas, such as the necessity of interdisciplinary approaches and the creation of transparent machine learning models. This paper contributes to the growing body of knowledge regarding machine learning applications in career counselling, offering scholars, practitioners, and policymakers’ insightful information about how cutting-edge ML techniques might enhance career decision-making and boost user happiness.