<p>Agriculture is vital to a country’s overall growth. Although the demand for food continues to rise in both quality and quantity, crop production is increasing at a slower pace. Plant diseases pose a significant threat to yield and food security, often remaining undetected until severe damage has occurred. Traditional detection methods are manual, weather-dependent, and require expert knowledge, which is rarely available in rural areas. To address these challenges, integrating the Internet of Things (IoT) and Artificial Intelligence (AI) enables early, automated diagnosis and continuous crop monitoring, improving yield and reducing environmental impact. We have gone through several literature review papers and seen that most focus on the role of (Artificial Intelligence) AI-based techniques such as (Machine Learning) ML and (Deep Learning) DL, while some are based on Internet of Things -based plant disease frameworks. We have seen in the review papers that no research has proposed a detailed taxonomy or rigorous dataset analysis, and most lack application-specific information on IoT sensors. To address this gap, the paper presents a structured taxonomy of (Machine Learning) ML, (Deep Learning) DL, and (Internet of Things) IoT-based approaches, discussing algorithms, datasets, and intelligent sensors in depth. It further analyzes the advantages, limitations, and performance trends of existing frameworks across plant factories, greenhouses, and smart farms. Finally, the review outlines the layered IoT architecture for plant disease detection, emphasizing data flow, communication protocols, and AI-driven decision-making, and concludes with key research gaps and future directions for developing adaptive and sustainable precision-agriculture systems.</p>