Insect behavior is a critical indicator of ecological dynamics, forensic relevance, and species-specific traits such as feeding, reproduction, and colonization. Traditional behavioral analysis often relies on manual observation, which is limited by subjectivity, temporal constraints, and scalability. This chapter explores the integration of artificial intelligence (AI) in decoding insect behavioral patterns through automated tracking, motion analysis, and interaction modeling. By leveraging computer vision, deep learning algorithms, and spatiotemporal modeling techniques, researchers can now analyze complex movement trajectories, interspecies interactions, and behavior-triggered responses in controlled and field environments. This chapter presents a comprehensive overview of AI-based methodologies, ranging from convolutional neural networks (CNNs) for real-time movement detection to recurrent neural networks (RNNs) for temporal behavior prediction. It critically evaluates existing models using performance metrics and case studies, emphasizing their accuracy, ecological validity, and potential forensic applications. Furthermore, the chapter addresses challenges including noise in environmental data, model overfitting, and the interpretability of behavioral outputs. This interdisciplinary synthesis positions AI as a transformative tool in behavioral entomology, with implications for forensic investigations, pest control, biodiversity monitoring, and environmental assessment. The chapter concludes with recommendations for standardized behavioral datasets, ethical AI deployment, and integrative research frameworks that combine biology and computational science.