Relevance. Early identification of gas hydrate formation risks is a critical task in the operation of field gas pipelines, as it depends on a wide range of dynamically changing system parameters. An additional complication in identifying the onset of gas hydrate crystallization processes lies in the heterogeneity of gas mixtures, their moisture content, and water ionic composition, which makes real-time monitoring virtually impossible when using existing software products that require significant time for data preparation and calculation. Aim. To demonstrate the feasibility of applying machine learning and deep learning algorithms, including the k-means method and multilayer perceptron, for analyzing hydrate formation risks in field gas pipelines under conditions close to real time. The study aims at evaluating the applicability of intelligent approaches to the task of predicting the onset of hydrate crystal formation, based on existing thermodynamic calculation methods. Results and conclusions. The results confirm the possibility of using neural network algorithms to predict the onset of gas hydrate formation processes and additionally demonstrate their strong generalization capabilities. For correct algorithm operation, the authors outline several key stages, including preprocessing of the input data, data analysis, training sample formation, hyperparameter selection, model training, and validation. Forecasting results obtained using the new algorithm developed by the authors, in comparison with existing mathematical models for hydrate formation calculation, indicate a high level of validity and justify the further integration of this approach into existing systems to automate the detection of gas hydrate formation. Timely detection and prevention of hydrate formation are aimed at ensuring the safe operation of field collection systems and improving the efficiency of management decisions to minimize associated risks.