The use of Artificial Intelligence (AI) in digital marketing is rapidly expanding, enabling highly personalized strategies for consumers. However, this advancement also raises serious concerns about data privacy, especially amid varying regulations such as the GDPR (Europe), CCPA (United States), and local policies across Southeast Asia. This study examines how AI technologies like Natural Language Processing (NLP) and predictive analytics can adaptively balance personalization with privacy protection. It also explores the emotional dimension of consumer responses—particularly trust and anxiety—and how these emotions shape perceptions of digital marketing strategies under different regulatory contexts. A mixed methods approach was employed, combining survey data from 400 respondents across three regions and in-depth interviews with 20 extreme-case participants. The analysis utilized tools such as SmartPLS, NVivo, and visual platforms like Tableau AI and MonkeyLearn. Findings reveal that limiting the collection of sensitive data can increase consumer acceptance by up to 23% without compromising marketing effectiveness. Consumer trust emerged as a key mediating factor, while anxiety amplified the demand for transparency. In Southeast Asia, incentive-based strategies were found to be 35% more effective than regulatory approaches. These findings underscore the importance of integrating technological, emotional, and cultural dimensions when designing ethical and context-aware digital marketing strategies.
