The booming world agriculture is racing against time to develop a few innovative solutions to meet ever-increasing demand for sustainable crop production, without sacrificing productivity due to pests. Conventional pest detection methods are largely manual and rely upon rules, which are laborious, error-prone, and impractical in most applications requiring large-scale field operations. Recent advances in deep learning pave the way for automated, accurate, and scalable solutions for pest detection and classification. This review paper gives an in-depth overview of the state-of-the-art deep learning approaches used for pest identification in field crops, shedding light on their architecture, performance metrics, and adaptability to diverse agricultural environments. Special emphasis is placed on convolutional neural networks (CNNs), transformer-based models, and hybrid frameworks that improve feature extraction and ensure better classification accuracies in real-world scenarios. Further, the review discusses challenges such as limited labeled datasets, class imbalances, computational constraints, and domain variability. This will pave the way toward efficient implementation by presenting potential strategies, including data augmentation, transfer learning, lightweight architecture, and edge deployment. As such, this work seeks to forge a synthesis of current progress and future open research directions in deep learning as applied in precision agriculture and resilient crops protection systems. By evolving such insights, the work intends to bring the researchers and practitioners on par with the creation of sustainable and intelligent pest management frameworks that will serve agriculture’s future.