Date palm (Phoenix dactylifera L.) constitutes one of the most economically and culturally significant crops across arid and semi-arid regions, yet its productivity faces existential threats from the Red Palm Weevil (Rhynchophorus ferrugineus, RPW) and a spectrum of fungal and bacterial diseases. While deep learning has demonstrated remarkable classification accuracies exceeding 97% in controlled laboratory environments, the transition from academic prototypes to deployable, real-time agricultural solutions remains critically underdeveloped. This comprehensive review systematically examines recent studies, synthesizing the current landscape of deep learning applications for RPW detection and date palm disease classification. Our analysis reveals a persistent disconnect between architectural sophistication and practical deployability. Furthermore, the literature exhibits a pronounced fragmentation between pest detection and disease classification, with few studies addressing the integrated palm health ecosystem. This review identifies critical research dimensions where the current state-of-the-art falls short. By mapping these interconnected gaps across the evaluated literature, this review establishes a structured roadmap for developing lightweight, accurate, and interpretable AI systems that bridge the gap between theoretical accuracy and operational feasibility in precision agriculture.