Identifying diseases in horticulture fruits is crucial in maintaining quality, reducing losses, and enhancing sustainable agricultural practices. Deep learning (DL) and machine learning (ML) techniques have enabled proficient and precise identification of these diseases. This paper consolidates the use of ML and DL approaches in horticultural fruit disease detection, incorporating the innovative models of convolutional neural networks (CNNs), Vision transformers, and other hybrid systems. It also reviews preprocessing and feature extraction for hyperspectral and multispectral imaging. Volume public datasets and real-world case studies are analyzed to demonstrate practical implementation and obstacles which include the quality of the dataset, required computation resources, and model interpretability. Furthermore, the paper elaborates on GAN-based data augmentation, implementing lightweight models on resource-constrained devices, and real-time IoT monitoring. Future directions aim at the utilization of explainable artificial intelligence, scaling up the models, and increasing sustainability in disease detection systems. The reviewed literature established this study as a point of reference for other researchers and practitioners to inspire the development of intelligent horticultural disease management systems.