Unexpected weather circumstances, including rain, hailstorms, draughts, and fog, often cause Apple leaf infections. This leads to a large decrease in farmer production. Early detection of apple leaf infections is crucial for preventing their onset and minimizing production losses. AI technologies assist in detecting and identifying apple tree leaf diseases, which may reduce infection, decrease chemical usage, enhance yield and quality, and preserve robust cultivar growth. This review study begins with apple leaf disease taxonomy, historical challenges, and aspirations. Then, it reviews deep and machine learning advances in apple leaf disease detection, classification, and segmentation. Image pre-processing was utilized, and its importance in feature extraction, the most common techniques used, and innovations are discussed. Afterwards, the available resources, dataset specification, time, and links are concerned. Later, an overall analysis, an outcomes comparison across various studies, and a discussion are offered. Finally, supply overview, challenges, and research opportunities. The research will assist in improving automated apple tree leaf disease systems by verifying economic benefits and supporting academic organizations’ advancement in technology and industry. This will improve overall quality and boost production.