Accurate, scalable classification of soil texture is essential for soil conservation, precision agriculture, land use planning, and restoration. Though precise, traditional laboratory and field methods are labor‑intensive, costly, and spatially constrained, limiting broad‑scale monitoring. This review synthesizes state‑of‑the‑art developments in artificial intelligence (AI)-based soil texture classification, evaluates their applications in soil conservation, and identifies persistent challenges and emerging opportunities. Recent AI techniques, including machine learning (ML), deep learning (DL), and multimodal data fusion, integrate spectral data, unmanned aerial vehicle (UAV) and satellite imagery, proximal sensing, and geospatial variables to facilitate high‑resolution mapping. Widely applied ML algorithms include Support Vector Machines (SVM), Random Forests (RF), k‑Nearest Neighbors (k‑NN), Decision Trees (DT), Artificial Neural Networks (ANN), and Gradient Boosting Machines (XGBoost). DL approaches, particularly Convolutional Neural Networks (CNN), Residual Network (ResNet), InceptionV3, and Transfer Learning (TL), extract hierarchical spatial–spectral features, achieving accuracies above 90%. Hybrid models combine complementary strengths, such as genetic algorithm‑optimized neural networks, CNN‑RF, and CNN‑RNN. Multimodal fusion of spectral, geospatial, and sensor data, including electromagnetic induction (EMI), ground penetrating radar (GPR), and portable XRF, further improves robustness and contextual relevance for erosion risk mapping, site‑specific nutrient management, precision farming, and restoration planning. Case studies, from across the globe, illustrate successful integration into adaptive land use strategies and climate‑resilient conservation. Key challenges span across data quality, sampling density, model interpretability, and cross‑regional generalizability. Emerging directions include Explainable AI (XAI), edge computing, crowdsourced datasets, and AI-enabled multimodal analysis. If responsibly implemented, AI‑driven soil texture classification provides a key pathway toward evidence‑based, sustainable soil management amidst changing climate.