Losses following harvest remain a major challenge in worldwide agriculture, particularly in developing countries such as India. Inadequate storage infrastructure, inefficient processing techniques, and poor supply chain coordination significantly reduce farmer income and food availability in these regions. This study provides a comprehensive examination of how artificial intelligence contributes to post-harvest technology, focusing on quality evaluation, classification and grading processes, dehydration methods, warehousing approaches, cold-chain logistics, and supply chain enhancement tactics. The farming industry faces fundamental challenges including limited productivity, scattered land holdings, and uncertain climate change effects. The research determines that tailored AI deployment can meaningfully reduce post-harvest waste while promoting environmentally sound and equitable agricultural food systems.