Global food security and agricultural output are greatly impacted by crop diseases and nutrient deficits. Manual inspections are the foundation of traditional detection techniques, yet they are laborious and prone to mistakes. Plant illnesses and deficiencies can be efficiently and scalablely diagnosed with machine learning (ML)-based systems. In this study, we use image processing and structured datasets to present a Random Forest-based model for precise crop disease and deficiency detection and classification. The model uses decision-tree ensembles to classify illnesses, extracts important information from leaf images, and recommends remedial measures. According to experimental findings, Random Forest performs better than conventional methods in terms of accuracy, resilience, and efficiency. This study offers an AI-powered early disease detection tool that enables farmers to increase agricultural yields and act promptly. The device continuously gathers information on plant physiology, soil health, and environmental factors in order to identify early warning signs of stress and potential threats. Predictive analytics allows for early interventions, allowing farmers to take corrective action before serious harm is done. The information is also easier to access thanks to the user-friendly interface and visualization tools, which empower farmers—especially smallholders who may lack technical expertise.