Indoor localization is essential for various modern applications, particularly in environments where traditional GPS-based positioning is unreliable. This paper introduces the ConFi framework, a confidence-aware system that combines UAV-assisted Wi-Fi fingerprinting with machine learning models to achieve ultra-precise indoor localization. By leveraging the high-speed, low-latency capabilities of 6G networks, ConFi ensures real-time, scalable, and adaptive positioning even in highly dynamic environments. The system utilizes UAVs equipped with Wi-Fi modules to dynamically collect accurate signal data, which is processed through a convolutional neural network (CNN) optimized for local environmental variations. Reinforcement learning is integrated into the system to enable continuous adaptation, enhancing the robustness of the framework. Experimental results show that ConFi achieves a test accuracy of 92%, outperforming traditional models like LSTM and LoRaWAN. The ConFi framework is suitable for real-world applications in complex indoor environments such as hospitals, airports, and multi-storey buildings, where precise and reliable indoor localization is required.