Abstract: Academic advising is essential for student success, yet traditional methods often struggle to address complex academic conditions such as irregular enrollment, prerequisite constraints, and limited course availability. This study proposes an Intelligent-Based Academic Advising Agents for Student Study Plan Recommender System using forward chaining algorithms to generate personalized study plans. The research aims to identify system features, examine the application of forward chaining in academic planning, and evaluate system acceptability using the ISO/IEC 25010. A mixed-methods approach was employed, integrating system development and evaluation. The forward chaining algorithm serves as a rule-based inference engine that processes student academic data to generate logical and adaptive course recommendations. The system was evaluated by students, academic advisors, and IT experts based on ISO/IEC 25010 quality characteristics. Results show that the system effectively supports both regular and irregular students by optimizing course sequencing, minimizing delays, and adapting to varying academic constraints. The system achieved a grand mean of 4.73 (Highly Acceptable), indicating strong performance in functionality, usability, reliability, and efficiency. The study concludes that the proposed system is a reliable decision-support tool that enhances academic advising, improves decision-making accuracy, and promotes timely student progression.