Abstract: The advancement of oil and gas exploration into complex environments such as deepreservoir, deepwater, and unconventional reservoirs has imposed significant challenges on traditional drilling fluids. The traditional drilling fluid performance in deep complex geologic environment cannot meet the stringent requirements of modern drilling technology. Intelligent-responsive materials, capable of adaptively modulating their properties in response to environmental stimuli, have emerged as pivotal technologies for enhancing drilling fluid performance. Concurrently, machine learning, as a powerful data-driven approach, has gained widespread application in material design and performance prediction, offering novel insights for optimizing intelligent-responsive materials. The integration of machine learning techniques into the study of intelligent-responsive drilling fluid materials not only expedites material screening and performance enhancement but also fosters the intelligent evolution of drilling. This advancement significantly boosts the efficiency and safety of oil and gas drilling operations, holding substantial theoretical value and broad application prospects. This paper systematically summarizes the classification and functional characteristics of intelligent responsive drilling fluid materials, with a focus on analyzing the structural composition and response mechanism of intelligent materials. It encompasses mainstream machine-learning algorithms such as artificial neural networks, support vector machines, and random forests, delineating their respective advantages. Through a comprehensive evaluation of existing research, the paper identifies critical challenges in the design of intelligent-responsive materials, including data insufficiency, limited generalizability of models, and challenges in experimental validation. Furthermore, it examines the potential of deep learning and multimodal data fusion technologies to enhance model accuracy and interpretability, underscoring the necessity of interdisciplinary collaboration in advancing the intelligent development of drilling fluids. In response to practical application requirements, the paper proposes future research directions for the integration of intelligent-responsive materials with machine-learning technologies, thereby providing a robust theoretical foundation and technical guidance for achieving efficient, safe, and environmentally sustainable oil and gas drilling operations.