<p>Due to climate change, permafrost regions are undergoing rapid evolution, posing a serious threat to roads, pipelines, foundations and other geotechnical infrastructure. Conventional methods for monitoring and predicting permafrost degradation have limitations in spatial coverage, temporal resolution and environmental dynamic adaptability. In recent years, the development of machine learning (ML) has opened up a new way to simulate the complex interaction between thermal state, soil properties and atmospheric variables in cold regions. This paper reviews the emerging applications of ML technology, from supervised learning models such as Random Forests (RF) and Support Vector Machines (SVM), to deep learning frameworks such as Convolutional Neural Networks (CNN), in predicting the thawing depth of permafrost, the evolution of ground temperature and the phenomenon of thermokarst. We systematically classify the application of ML according to the input data types (remote sensing, in-situ sensors, satellite climate data) and geotechnical output variables (thermal conductivity, soil strength, bearing capacity), and discuss the practice of combining ML with physical process model to enhance the interpretability and generalization ability. This review pays special attention to the risk of soil weakening, foundation instability and infrastructure failure caused by permafrost melting in the Arctic and subarctic regions. Moreover, this paper points out the key challenges such as data scarcity, lack of cross regional mobility and lack of uncertainty quantification. By systematically integrating the latest research results, data sources, model architecture and evaluation indicators, this review provides a basic reference for researchers and practitioners engaged in climate adaptive geotechnical engineering. The research results highlight the potential of ML as a transformative tool in permafrost geotechnical engineering, thereby facilitating environmental monitoring, risk assessment and infrastructure planning.</p>