Indoor navigation remains a significant challenge for visually impaired individuals, requiring accurate room classification and precise user localization. In this paper, we propose an AI-guided system that combines Convolutional Neural Networks (CNNs) for room classification with a depth-based Kalman filter for user localization. Our approach leverages RGB images for room classification and depth images for real-time user position estimation, eliminating the need for additional sensors or complex sensor fusion. We implement and evaluate our system using the NYU v2 indoor dataset. The CNN achieves 90% accuracy, outperforming a Multi-Layer Perceptron (MLP), which attains 74% accuracy. For user localization, we compare a depth-based Kalman filter with a Particle Filter. The Kalman filter demonstrates lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), ensuring more precise and stable position estimates. While the system is not yet deployed in real-world applications, our findings highlight its potential for practical use in smart homes, assistive navigation, and augmented reality.