<![CDATA[This research aims to develop an AI-driven data engineering model tailored for adaptive billing systems in the realm of digital economy monetization. As digital services evolve, traditional billing mechanisms often struggle to accommodate the varied demands of users. The model proposed integrates user profiles, transaction histories, and payment patterns, facilitating precise cost assessments that respond dynamically to user behaviors. A descriptive qualitative methodology was employed, concentrating on the framework of the model, system workflows, and essential analytical functions. Findings indicate that this approach enhances both accuracy and transparency in billing processes while fostering stronger relationships between businesses and their clients. By utilizing artificial intelligence, the system adapts to shifts in consumption patterns, identifies anomalies, and adjusts pricing based on real-time usage. These insights lay the groundwork for developing more agile and efficient billing solutions, offering a practical roadmap for digital enterprises aiming to optimize revenue management based on service utilization.]]>
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