Career planning and skill development for students have become increasingly complex due to rapidly evolving industry demands and fragmented learning resources. Existing career guidance systems largely rely on static and generic recommendations, offering limited personalization and adaptability. To address these challenges, this paper proposes an AI-driven career planning and self-development platform that delivers dynamic and personalized guidance tailored to individual students. The proposed system employs a hybrid recommendation framework that integrates collaborative filtering and content-based filtering, enhanced by neural networks for skill-gap analysis. Furthermore, Gemini API integration enables contextual academic project suggestions and adaptive career roadmaps aligned with students' interests and long-term career objectives. By analyzing academic history, learning behavior, and personal preferences, the platform recommends relevant projects, skill pathways, and curated free and paid learning resources. Experimental evaluation demonstrates improved recommendation accuracy and higher user relevance compared to traditional rule-based career guidance approaches. The results indicate that the proposed platform effectively bridges the gap between academic learning and industry readiness, providing a scalable and intelligent solution for student career planning and self-development.