This study presents a systematic review of artificial intelligence (AI) and Internet of Things (IoT) applications in post-harvest fruit management. The review was conducted in accordance with the PRISMA 2020 guidelines. Of the 379 initial studies identified, 43 were analysed in detail. The analysis revealed that deep learning models, such as convolutional neural networks (CNNs) and You Only Look Once (YOLO) architectures, consistently achieved mean accuracies above 90% (an F1 score of approximately 0.89–0.92) when performing tasks such as grading fruit, classifying its maturity, and detecting damage. Spectroscopic and luminescence-based methods demonstrated comparable performance (approximately 92% accuracy), albeit with greater variability under tropical field conditions. Conversely, digital twin–IoT integrations substantially enhanced shelf-life prediction and waste reduction when robust cold-chain infrastructure was in place. However, Latin American studies highlighted structural constraints, such as limited connectivity, high costs, and fragmented supply chains, which restrict scalability despite promising results in controlled trials. The review also identified gaps in existing systematic reviews, which often focus on either broad agricultural digitalisation or biological aspects without addressing post-harvest AI. Finally, we propose minimum reporting recommendations, including the use of confusion matrices, confidence intervals, and domain shift evaluations, to strengthen the reproducibility and ethical adoption of AI in diverse agricultural contexts.