One of the most persistent threats to the world’s food security is insect pests, which lead to significant crop losses and reliance on chemical pesticides. Traditional monitoring techniques such as field surveys, pheromone traps, and manual scouting are frequently time-consuming, unreliable, and inadequate for early intervention. Artificial intelligence (AI) and recent developments in smart sensing technologies have revolutionized pest monitoring by giving farmers and researchers unheard-of precision in identifying, categorizing, and forecasting pest dynamics. These sensors produce continuous data streams that may be handled at the edge or in the cloud when linked to IoT platforms, which lowers latency and energy requirements. Climate data, soil parameters, and biological signals can now be combined to create AI-driven prediction models that estimate pest population dynamics by employing machine learning, deep learning, and ensemble techniques. The change from reactive to predictive pest management has the potential to reduce environmental effects, unnecessary pesticide use, and increase resistance to pest pressures caused by climate change. However, there are still many obstacles to overcome, such as the need for reliable datasets, energy-efficient sensor networks, open governance structures, and methods to overcome smallholder farmers’ adoption hurdles. AI-enabled smart sensing has the potential to revolutionize sustainable agriculture and global food security by combining technical advancements with fair access and supporting regulatory frameworks.