Agriculture is a cornerstone of human survival and economic development, especially in developing countries where it represents a significant portion of national revenue. This sector’s evolution has profoundly improved health and living standards. However, agriculture faces critical challenges from plant and crop diseases, causing significant productivity losses and staggering global economic impacts estimated at $898 billion. The escalating population and changing climate patterns intensify food security issues, underscoring the urgent need for early disease detection to sustain agricultural productivity and mitigate economic losses. Recent breakthroughs in artificial intelligence (AI), machine learning (ML), and computer vision have revolutionized crop disease forecasting, presenting groundbreaking solutions. This systematic review delivers an in-depth analysis of current technologies employed in disease detection for a wide range of food crops, including fruits, vegetables, and cereals. It synthesizes findings from an exhaustive global literature search, focusing on studies leveraging ML techniques, convolutional neural networks (CNN), and diverse deep learning (DL) architectures for classification and regression analyses. This review is anchored in a meticulous examination of 600 papers, with comprehensive insights distilled from 40 primary research articles, demonstrating a rigorous and focused selection process.