The advancement of information technology encourages higher education institutions to optimize student achievement data through intelligent and interactive dashboards. At Telkom University’s Faculty of Industrial Engineering, the current Belmawa competition dashboard is limited to static tables and lacks Artificial Intelligence (AI) capabilities for data-driven recommendations, resulting in inefficient evaluation and decision-making processes. This research aims to develop an AI-integrated dashboard system to support analysis of student competition performance and strategic recommendation generation. The development uses Waterfall methodology within the Software Development Life Cycle (SDLC), including stages of requirement analysis, design, implementation, validation, and maintenance. The system architecture comprises four layers: a Data Layer (Aiven MySQL with SSL), a Visualization Layer (Google Looker Studio), an Intelligence Layer (Flowise AI with OpenAI GPT-4o for natural language processing and SQL conversion), and a Presentation Layer (Laravel with MVC pattern). A total of 867 records from 6,642 competition data entries were used after preprocessing. The dashboard features interactive visualizations and AI chatbot that responds to natural language input in Indonesian or English, converting it into SQL and providing analytical insights. The system also supports role-based access control and full CRUD functionality for competition data management. System validation achieved 100% success across automated testing (Laravel Dusk), user acceptance testing, and usability evaluation. The System Usability Scale (SUS) resulted score of 90.0, categorized as “Best Imaginable.” This research contributes to the development of academic information systems by demonstrating how the integration of dashboards, artificial intelligence, cloud technologies, and waterfall methodology enhance data-driven decision-making in higher education.