The rapid intensification of global agriculture, coupled with climate variability and the spread of invasive pests, poses unprecedented challenges for crop protection. Traditional pest surveillance and control strategies are becoming increasingly inadequate for capturing the complexity and dynamism of pest populations. Advances in artificial intelligence (AI), machine learning (ML), and digital technologies now offer transformative opportunities to enhance pest monitoring, forecasting, and management. By integrating multidimensional datasets ranging from meteorological variables, crop phenology, and soil conditions to pest genomics and movement ecology, AI-driven systems can generate predictive insights and enable proactive interventions. Emerging tools, such as smart traps, edge-enabled recognition systems, mobile-based crowdsourcing platforms, and digital twins, exemplify the convergence of field-level data collection with real-time analytics, supporting both localized decision-making and large-scale risk assessment. Predictive models, including recurrent neural networks (RNNs) and long short-term memory (LSTM) architectures, are particularly effective in capturing temporal dependencies in pest dynamics, whereas participatory surveillance platforms empower farmers and extension agents to contribute to adaptive monitoring networks. Ethical considerations, including data ownership, equity, and accessibility, remain central to ensuring that AI-powered pest management benefits smallholder systems along with technologically advanced farms. This chapter synthesizes current innovations and highlights pathways for integrating AI, big data, and ecological knowledge into sustainable climate-resilient pest management frameworks. Ultimately, the strategic adoption of AI-enabled crop protection tools can play a decisive role in safeguarding global food security while reducing the environmental footprint.