This paper introduces GraphLearner, a web-based platform designed to simplify access to advanced artificial intelligence (AI) through intuitive, graph-based data visualization and real-time prediction generation. The primary objective of the project is to bridge the gap between complex neuromorphic AI systems and non-expert users by offering an interactive interface for understanding and manipulating AI predictions. The platform was developed using a combination of Flask, HTML, JavaScript, and various frontend libraries to ensure a responsive and user-friendly experience. The methodology involved testing the platform across several datasets to assess its usability, adaptability, and performance. Results showed that GraphLearner significantly enhances ease of use when working with single-domain datasets, making AI outputs more understandable for non-technical users. However, challenges emerged when handling larger, multi-domain datasets, particularly in terms of data storage and performance scalability. These limitations highlight opportunities for future improvements, including optimizing data handling processes and enhancing user interaction features. The study concludes that GraphLearner has the potential to democratize AI by lowering technical barriers, fostering broader adoption in fields requiring data-driven insights.