Soil erosion is a major environmental challenge, threatening agricultural productivity and ecosystem stability. This study examines the integration of artificial intelligence (AI), remote sensing (RS), Geographic Information Systems (GIS), and Unmanned Aerial vehicles (UAVs) for accurate and efficient erosion assessment to support sustainable land management. RS provides large-scale, multi-temporal data on land degradation, while GIS enables spatial analysis of key factors such as slope, land cover, and rainfall intensity. UAVs offer high-resolution, real-time monitoring of terrain changes often missed by traditional methods. Combining these technologies enhances predictive models like the Revised Universal Soil Loss Equation (RUSLE), strengthened through machine learning for dynamic risk mapping. UAV-derived digital elevation models (DEMs) improve watershed-scale assessments, while satellite imagery (Sentinel-2, Landsat) supports regional monitoring. AI techniques, including machine learning (ML) and deep learning (DL), further advance detection, classification, and modeling of erosion by processing multispectral and hyperspectral imagery. These algorithms also predict erosion-related parameters such as slope, vegetation, soil moisture, and texture. Despite challenges such as data resolution and computational demands, the synergy of RS, GIS, UAVs, and AI offers scalable, cost-effective solutions. This integrated approach empowers policymakers and farmers with actionable insights, driving sustainable soil conservation globally.