The increasing global population, combined with climate variability and finite natural resources, poses significant challenges to modern agriculture. Conventional farming practices are often labor-intensive, inefficient, and lack real-time monitoring of crop health and soil conditions. Precision agriculture has emerged as a solution by integrating advanced technologies such as unmanned aerial vehicles (UAVs), Internet of Things (IoT) sensors, and artificial intelligence (AI). In this work, we propose a drone-based precision agriculture framework that utilizes highre-solution aerial images and cloud-based deep learning pipelines to automate key farm management operations. The system focuses on four main objectives: (i) detecting water scarcity to enable responsive irrigation, (ii) mapping and identifying weeds, (iii) recognizing plant diseases and suggesting appropriate treatments, and (iv) monitoring crop growth to provide accurate yield predictions. The proposed framework aims to improve efficiency, reduce resource wastage, and support data-driven decision-making in agriculture. The proposed system employs a DJI mavic 2 pro UAV equipped with a 4K,60fps camera to capture high-resolution imagery during scheduled flights. Captured images are transmitted to the cloud, where advanced machine learning algorithms process them to generate actionable insights. Experimental results demonstrate strong performance, with 94.32% accuracy in growth estimation, 97.84% accuracy in weed detection, and 93.87% accuracy in disease identification with recommended treatments. The findings indicate that the framework effectively reduces manual labor, optimizes resource usage, and enhances crop productivity. By delivering real-time insights to farmers through mobile applications, this approach supports data-driven decision-making, promotes sustainable agricultural practices, and contributes to addressing global challenges of food security and resource management.