In modern political science research, increased attention is being paid to the analysis of the possibilities and limitations of using artificial intelligence (AI). The evolution of AI methods from neural networks to generative artificial intelligence (AGI) models is considered. The author systematizes the main AI tools for political analysis: machine learning, deep learning, neural networks of various architectures, natural language processing (NLP). An overview of software platforms for collecting, processing and visualizing political data is presented. Key thematic areas of AI application are highlighted: electoral research, political discourse analysis, international relations and geopolitics studies, public administration. The capabilities of the NLP method for sentiment analysis, topic modeling and extraction of main ideas from political texts are outlined. The author not only emphasizes the advantages of using AI in political analysis (automation, reducing research time, new forecasting capabilities), but also shows the risks and limitations: technical vulnerabilities of models, the phenomenon of “AI hallucinations”, bias of algorithms, problems of transparency and the ability to interpret results, ethical and legal challenges. The position of “cautious optimism” is substantiated, which assumes the effective use of AI while maintaining systematic analysis, norms of critical reflection, ethical principles and maintaining the professional competencies of a political scientist. The conclusion is drawn about the need for the responsible use of AI in political research, mastery of modern digital analytics tools and the mandatory retention of skills in traditional methods of studying the sphere of politics.