The rapid expansion of internship programs across India has created a need for intelligent systems capable of efficiently matching students with relevant opportunities. The Prime Minister Internship Scheme aims to provide large-scale internship access to youth across diverse academic backgrounds and industries. However, the effectiveness of such initiatives depends heavily on accurate candidate–internship matching. Traditional filtering methods based on degree, location, or basic qualifications often fail to capture deeper relationships between candidate skills, interests, and company requirements. This research proposes an Artificial Intelligence based Internship Recommendation Engine designed to improve internship allocation within the PM Internship Scheme ecosystem. The system leverages natural language processing for resume analysis, machine learning techniques for candidate–role matching, and hybrid recommendation strategies combining content-based and collaborative filtering. By analyzing candidate profiles and internship descriptions, the model generates personalized internship recommendations ranked by relevance scores. Experimental evaluation demonstrates that the proposed system improves recommendation accuracy and increases the likelihood of successful internship placements. The framework also supports scalability for large datasets, making it suitable for national-scale internship platforms. The study highlights how AI-driven recommendation systems can enhance employability initiatives by optimizing internship discovery and improving alignment between student competencies and industry needs.