G. Sidhu precision agriculture, smart farming, technologies, applications, challenges 66-78 9 (3) July 2025 Modern agriculture is being revolutionized by precision and smart farming, which use cutting-edge technologies to maximize input use, boost productivity, and improve environmental sustainability. In the context of crop monitoring and management, this study summarizes current advancements in unmanned aerial vehicles (UAVs), remote sensing, artificial intelligence (AI), machine learning (ML), the Internet of Things (IoT), and decision support systems (DSS). High-resolution, real-time evaluations of vegetation health, disease outbreaks, and water stress are made possible by the combination of multispectral, hyperspectral, thermal, and RGB sensors with UAVs. Predictive decision-making in agriculture is supported by AI and ML techniques such as Random Forests, Support Vector Machines, Convolutional Neural Networks, and Long Short-Term Memory networks. This review also examines how cloud computing, GIS platforms, and wireless sensor networks facilitate real-time monitoring and geographical analysis. Global case studies highlight benefits like enhanced yields, decreased environmental impact, and increased resource efficiency. Cost, data privacy, farmer uptake, and technical limitations continue to be obstacles, though. Emerging trends like robots, edge computing, and satellite-UAV data fusion are discussed in the reviewand#39;s conclusion, along with important research gaps and legislative requirements to encourage wider implementation. Thus, precision farming presents a promising path toward sustainable intensification and climate-resilient food systems.