Wireless sensor networks (WSNs) are essential for collecting, transmitting, and processing large volumes of data to satisfy specific application objectives. However, managing this extensive data remains a significant challenge. Data fusion emerges as a targeted solution to address this issue, enabling the integration of information from multiple sensors for more accurate results. Artificial intelligence (AI) enhanced data fusion by enabling intelligent processing, analysis, and error reduction, optimizing data, and improving real-time decision-making. Several industries use AI-based data fusion, including weather forecasting, agriculture and healthcare. Despite advancements, existing data fusion methods still face limitations, including the difficulty of handling large data volumes in real time, managing energy consumption, and ensuring algorithm scalability in dynamic WSN environments. Traditional fusion methods often require high resource usage and may not be well-suited for such environments, where precision is critical. In order to address these issues, we suggest a solution utilizing AI algorithms to improve the data fusion process to reduce energy consumption and maintain data accuracy. In conclusion, integrating AI with data fusion in WSNs holds promising potential, but further work is needed to optimize AI algorithms in resource-constrained settings. Future studies should concentrate on enhancing the performance of large-scale WSNs and using AI in practical settings to improve data management in a variety of industries.