Choosing the right career path and the right job opportunity has become increasingly difficult in a labour market where industry requirements evolve faster than academic curricula and where the sheer volume of job postings makes manual evaluation impractical for most candidates. This paper presents the AI-Based Career Advisor, an intelligent system designed to help individuals understand how well their resume aligns with a target job description, identify missing skills, and receive concrete, personalized guidance for improving their employability. The system combines a supervised machine learning model with natural language processing and large language model components to deliver this guidance in a single, integrated workflow. At its core is a resume–job description fit classifier trained on 6,241 real-world resume–job pairs sourced from a public dataset, using TF-IDF based feature engineering across 10,012 dimensions. Six candidate algorithms — Logistic Regression, Naive Bayes, Support Vector Machine, Random Forest, a Neural Network, and XGBoost — were trained and compared, with XGBoost emerging as the best-performing model after hyperparameter optimization, achieving 78.14% test accuracy and an 89.57% ROC-AUC score. The system further incorporates a hybrid skill-extraction pipeline built on spaCy\'s named entity recognition and phrase matching, a GPT-4-based resume enhancement module accessed through LangChain, and supporting modules for learning-resource and project-idea recommendation. The complete pipeline is deployed as an interactive Streamlit web application, giving users real-time predictions and actionable career feedback. This paper discusses the motivation, design, methodology, and evaluation of the system, and outlines directions for extending it into a more comprehensive career guidance platform.