The global insect biodiversity crisis demands scalable monitoring solutions. Passive Acoustic Monitoring (PAM) generates vast data through insect sounds, but analyzing this “data deluge” requires automated methods. This review outlines the deep learning (DL) pipeline for insect bioacoustics, including data acquisition, pre-processing (denoising, segmentation), and feature representation (e.g., spectrograms and adaptive frontends such as LEAF). Dominant architectures include Convolutional Neural Networks (CNNs), Convolutional-Recurrent Neural Networks (CRNNs) for capturing temporal patterns, and Foundation Models (FMs) that enable transfer learning to address data scarcity. DL supports key applications including biodiversity assessment, precision pest management, pollinator monitoring, and behavioral analysis. Critical challenges persist, particularly limited labeled data, reduced model robustness in noisy environments, and computational constraints. Future research must advance beyond detection toward quantitative abundance estimation and strengthen interdisciplinary integration. This review synthesizes recent advances, identifies persistent gaps in generalization and ecological inference, and presents a roadmap for transitioning from detection-based systems to quantitative ecological monitoring.