In today’s competitive employment landscape, identifying an optimal career path that aligns with an individual’s skills, education, and interests is a significant challenge. Traditional career counseling methods often rely on manual evaluation, leading to limited scalability and subjective recommendations. This study provides an in-depth examination of AI-driven methods used in career and job recommendation systems, with a particular focus on the contributions of NLP and transformer-based architectures. Existing models that utilize traditional machine learning techniques like Support Vector Machines and Random Forest have achieved moderate accuracy in skill- based classification, yet they fall short in capturing contextual meaning. Recent advancements in transformer architectures, particularly Sentence-BERT, have enabled deeper semantic analysis of resumes and job descriptions, improving matching accuracy. The proposed hybrid framework combines resume feature extraction, job-role matching, and personalized course suggestions by leveraging cosine similarity and transformer-based embeddings. This review highlights current methodologies, identifies research gaps, and outlines the effectiveness of transformer-based hybrid models in delivering precise, scalable, and adaptive career recommendations for diverse professional domains.