Indoor localization using WiFi fingerprinting has gained significant a ttentiond ue toi tss calability a nd costeffectiveness; however, achieving high accuracy, robustness, and computational efficiency r emains c hallenging. T his paper presents a set of targeted enhancements to the FasterKAN frame-work for indoor localization, aimed at improving both predictive performance and deployment efficiency. The proposed improvements include refined access-point filtering strategies, an optimized activation function design, GPU-accelerated training, and a more stable regression modeling approach. The enhanced FasterKAN framework employs a single unified model to simultaneously perform three tasks: coordinate regression for estimating user latitude, longitude, and altitude; floor and building classification; and space-ID classification within predefined indoor regions. Experimental results demonstrate consistent performance gains over the original FasterKAN implementation, achieving a mean absolute localization error of 3.40 m compared to 3.56 m. Classification accuracy is improved from 99% to 99.3% for the floor/building task and from 71% to 72.46% for the space ID task. Furthermore, the proposed implementation achieves substantial computational advantages, delivering inference speeds that are 2.7 times faster than convolutional neural networks on CPUs and 7-8 times faster on GPUs. These results confirm that the optimized FasterKAN framework offers a robust, accurate, and computationally efficient alternative to convolution-based models, making it well suited for large-scale and real-time indoor localization deployments.