To systematically review the research progress of intelligent fracturing technology in unconventional oil and gas reservoirs, by integrating machine learning algorithms (e.g., random forest and gradient boosting), the embedded discrete fracture model (EDFM), fiber-optic/microseismic monitoring technologies, and smart equipment, this study analyzes technological breakthroughs and application cases in key aspects such as reservoir parameter prediction, fracture propagation simulation, and real-time control. Results demonstrate that the random forest and gradient boosting models demonstrated optimal performance in permeability prediction (R²>0.92). The EDFM-AI workflow reduces fracture parameter calibration errors to 6.8%. Fiber-optic monitoring technology achieves sub-millimeter resolution in fracture detection. The intelligent early-warning system predicts sand plugging risks 30 seconds in advance (accuracy above 85%). Intelligent fracturing technology significantly enhances reservoir modification efficiency and production, but challenges such as small-sample generalization, multi-source data fusion, and equipment autonomy require further resolution. Establishing a closed-loop technical system encompassing “reservoir evaluation, optimized design, fracture monitoring, anomaly prediction, and equipment control”, and promote the development of intelligent and precise fracturing processes.