Recent advancement in wireless communications and electronics has enabled the development of Wireless Sensor Networks (WSN). WSNs are being deployed in a variety of location-aware applications, where the measurement of data is meaningless without accurate location. Many localization algorithms have been proposed in order to increase the accuracy and minimize the cost requirements. Artificial intelligence techniques such as fuzzy logic and neural networks can be utilized as effective methods to satisfy these requirements. In this paper, we present two efficient artificial intelligence-based localization algorithms for WSNs. In the first algorithm, we implement a Sugeno-type fuzzy system with a collaborative communication feedback to achieve an accurate and cost effective two-dimensional (2D) localization system. In the second algorithm, the idea of the 2D localization using neural network is extended to achieve a three-dimensional (3D) localization with simplicity, location accuracy, and low cost. The proposed approach is able to localize mobile nodes with unpredictable movement patterns. The simulation results depict the performance and the effectiveness of each approach.