The integration of artificial intelligence (AI), machine learning (ML), deep learning (DL), and the Internet of Things (IoT) is transforming the agricultural sector by enhancing productivity, optimizing resource utilization, and improving decision-making processes. These technologies address critical challenges in agriculture, such as crop monitoring, pest detection, weather forecasting, and soil analysis. Key findings in this paper reveal the diverse applications of IoT and AI-based technologies, such as predictive analytics for anticipating disease outbreaks, optimization of planting schedules, and real-time insights into crop and livestock health using deep learning models. Case studies highlight the success of smart farming systems in improving precision agriculture, supply chain management, and market research. The seamless integration of AI with IoT devices further enhances traceability and operational efficiency, while advancements in edge computing enable localized and real-time decision-making. Despite these benefits, limitations such as high implementation costs, data privacy concerns, and the need for technical expertise pose challenges. The systematic adoption of these technologies, particularly during the pandemic, has demonstrated their transformative potential, paving the way for sustainable and efficient farming practices.