Job recommendation systems are platforms systems that utilize AI to give job recommendations to users based on experience, skills, and interests. The systems face challenges including sparse data, data security and privacy, prevention of bias and discrimination, and explainability and interpretability. Deep learning, reinforcement learning, and knowledge graphs are the emerging technologies that address these challenges to improve the precision of the recommendations and explainability of the system. The matching of skills to labor market needs is becoming increasingly complex as foundational competencies transform in unforeseeable manners, posing a prime challenge for learning in the 21st century. With the greater volume of data, demand-side and supply-side (open educational resources) as well as the application of intelligent technologies, there is potential to address the problem. This paper proposes a novel AI-driven approach to create an open, personalized, and labor market-focused job recommendation system, referred to as the AI-powered recommendation system for education and career analytics. The findings aim to enhance the job search experience, reduce the time needed to find suitable opportunities, and increase job satisfaction. The proposed architecture acts as a mediator, and this work provides a comprehensive review of various filtering, machine learning, and deep learning techniques that have transformed job recommendation systems, along with a brief discussion of their applications and challenges.