This review critically examines the advancements and challenges in nanostructured tungsten oxide (WO3) gas sensors. It highlights significant improvements in sensor sensitivity for gases like NO2, NH3, and VOCs, achieving detection limits in the ppb range. The review explores innovative approaches such as doping WO3 with transition metals, creating heterojunctions with materials like CuO and graphene, and using machine learning to optimize sensor configurations. Key challenges include cross-sensitivity to different gases, particularly at higher temperatures, and long-term stability affected by grain growth and dopant volatility. Potential solutions include statistical analysis of sensor arrays, surface functionalization, and novel nanostructures for enhanced performance and selectivity. The impact of ambient humidity on sensor performance and strategies to mitigate it, such as using humidity-shielding composite materials and hydrophobic surface functionalization, are also discussed. The review addresses the need for high operating temperatures and power consumption, proposing advanced materials and new transduction principles to lower temperature requirements. Finally, it emphasizes the necessity for a multidisciplinary approach combining materials synthesis, device engineering, and data science to develop next-generation WO3 sensors with enhanced sensitivity, ultrafast response rates, and improved portability. The integration of machine learning and IoT connectivity is highlighted as a key driver for new applications in areas like personal exposure monitoring, wearable diagnostics, and smart city networks, underscoring WO3's potential in future technological advancements.This review critically examines the advancements and challenges in nanostructured tungsten oxide (WO3) gas sensors. It highlights significant improvements in sensor sensitivity for gases like NO2, NH3, and VOCs, achieving detection limits in the ppb range. The review explores innovative approaches such as doping WO3 with transition metals, creating heterojunctions with materials like CuO and graphene, and using machine learning to optimize sensor configurations. Key challenges include cross-sensitivity to different gases, particularly at higher temperatures, and long-term stability affected by grain growth and dopant volatility. Potential solutions include statistical analysis of sensor arrays, surface functionalization, and novel nanostructures for enhanced performance and selectivity. The impact of ambient humidity on sensor performance and strategies to mitigate it, such as using humidity-shielding composite materials and hydrophobic surface functionalization, are also discussed. The review addresses the need for high operating temperatures and power consumption, proposing advanced materials and new transduction principles to lower temperature requirements. Finally, it emphasizes the necessity for a multidisciplinary approach combining materials synthesis, device engineering, and data science to develop next-generation WO3 sensors with enhanced sensitivity, ultrafast response rates, and improved portability. The integration of machine learning and IoT connectivity is highlighted as a key driver for new applications in areas like personal exposure monitoring, wearable diagnostics, and smart city networks, underscoring WO3's potential in future technological advancements.