<![CDATA[Digitalization, changing customer behavior, and increased competition have put the telecom industry in rapid motion. Conventional fixed pricing models are no longer able to keep pace with the evolving requirements of today's telecom consumers. This paper examines the deployment of real-time dynamic pricing strategies in telecommunications utilizing artificial intelligence. By using machine learning algorithms and big data analytics, operators can collect and analyze user behavior, network utilization, competitor dynamics, and market demands in real-time, thereby making pricing decisions more informed and optimal. The study also highlights critical AI methods, including reinforcement learning, clustering, and predictive analytics, employed to facilitate adaptive pricing strategies. Examples of telecom operators that have implemented AI-based pricing models are provided and analyzed in terms of enhanced revenue, increased customer retention, and improved network utilization. The results show that AI-based dynamic pricing is not only beneficial to profitability but also conducive to personalized and efficient customer service. However, implementation faces barriers such as data confidentiality, regulatory constraints, and computationally intensive infrastructure. This paper offers recommendations for future adoption and emphasizes the importance of ethical AI practices, transparency, and continuous control in deploying dynamic pricing models in the telecom industry.]]>
