Industry 4.0 and artificial intelligence are shown to bring great change or disappear a large proportion of work, while new jobs are born With the dynamical background comes the demand for a smart career guidance system, which will provide advice that is personalized, reliable, and adaptive. This paper systematically reviews the literature on the application of machine learning (ML) and explainable artificial intelligence (XAI) to career guidance. On the basis of PRISMA guidelines, 847 documents published between 2019 and 2025 were carefully screened, and 95 high-quality articles were extracted for in-depth review. The review classifies main ML methods—including collaborative filtering, content-based filtering, deep learning architectures such as LSTM, Transformer, and GNN, reinforcement learning, and their performance, limitations, and interpretability were judged. In parallel, the author analyzes such essential but questioned XAI techniques as LIME, SHAP, attention mechanisms, decision rules and counterfactual explanations in terms of their transparency and perceived user trust, as well as how easily acted upon these explanations are. From these foundations, the paper presents a five-layer hybrid ML-XAI framework that integrates data processing, knowledge maps, ensemble ML models, multi-level explanations, and user-centered presentation. In addition to these, future developments, such as flat or formidable language models and federated learning for maintaining privacy and fairness-aware algorithms, are explored, together with key challenges for further research. All in all, the paper provides a structured basis and practical guidance for next-generation, intelligent, transparent, and equitable career guidance systems.