The article discusses the development of a toolkit for global insect biodiversity monitoring, emphasizing the integration of four major technological approaches: molecular methods, computer vision, autonomous acoustic monitoring, and radar-based remote sensing. These technologies have the potential to revolutionize insect ecology and enable comprehensive, fine-grained monitoring across the globe. However, current advancements have largely been developed in isolation, leading to redundancy and methodological sprawl. The theme issue highlights recent developments and applications of these technologies, outlining the way forward for data processing, cost-effectiveness, trend analysis, technological integration, and open data requirements. Molecular methods use DNA or RNA from insects to identify species, offering insights into their ecological roles and genetic diversity. Computer vision enables automatic identification of insects using images, often to species or genus level, and is increasingly used in monitoring programs. Autonomous acoustic monitoring uses sound recordings to detect and identify insects, providing valuable data on their activity and distribution. Radar-based remote sensing detects insects in the air, offering insights into their biomass and flight patterns. The article also addresses challenges in implementing these technologies, including the need for standardized methods, data sharing, and the development of reference libraries for DNA, images, and sounds. It emphasizes the importance of interdisciplinary collaboration and the need for advanced computational resources and skills. The article concludes with a vision of an integrated insect monitoring system that combines these technologies to provide comprehensive data on insect biodiversity, enabling informed conservation and management actions. The system would involve multi-sensor stations, automated devices, and data sharing to ensure the availability of essential biodiversity variables (EBVs) in near-real time. The article also highlights the importance of involving citizen scientists and the need to address the challenges of monitoring rare and hard-to-detect species.The article discusses the development of a toolkit for global insect biodiversity monitoring, emphasizing the integration of four major technological approaches: molecular methods, computer vision, autonomous acoustic monitoring, and radar-based remote sensing. These technologies have the potential to revolutionize insect ecology and enable comprehensive, fine-grained monitoring across the globe. However, current advancements have largely been developed in isolation, leading to redundancy and methodological sprawl. The theme issue highlights recent developments and applications of these technologies, outlining the way forward for data processing, cost-effectiveness, trend analysis, technological integration, and open data requirements. Molecular methods use DNA or RNA from insects to identify species, offering insights into their ecological roles and genetic diversity. Computer vision enables automatic identification of insects using images, often to species or genus level, and is increasingly used in monitoring programs. Autonomous acoustic monitoring uses sound recordings to detect and identify insects, providing valuable data on their activity and distribution. Radar-based remote sensing detects insects in the air, offering insights into their biomass and flight patterns. The article also addresses challenges in implementing these technologies, including the need for standardized methods, data sharing, and the development of reference libraries for DNA, images, and sounds. It emphasizes the importance of interdisciplinary collaboration and the need for advanced computational resources and skills. The article concludes with a vision of an integrated insect monitoring system that combines these technologies to provide comprehensive data on insect biodiversity, enabling informed conservation and management actions. The system would involve multi-sensor stations, automated devices, and data sharing to ensure the availability of essential biodiversity variables (EBVs) in near-real time. The article also highlights the importance of involving citizen scientists and the need to address the challenges of monitoring rare and hard-to-detect species.