Artificial intelligence (AI) is transforming how we create data visualizations, but a major limitation remains—most AI tools produce generic visuals that ignore cultural differences in interpretation. Colors, symbols, layouts, and even how data is presented can mean different things across cultures, leading to misunderstandings or exclusion. Our research explores how cultural background affects how people understand AI-generated visuals and introduces a new approach to designing adaptive visual analytics systems that respect cultural diversity. Using a combination of methods—including cross-cultural user testing, computational analysis of AI-generated visuals, and designer interviews—we uncover cultural biases in current tools (such as Western-centered color meanings or left-to-right flow assumptions). We then develop and evaluate a prototype AI model that customizes visual elements (like color schemes or legend placement) based on a user’s cultural background. Our results show that culturally adapted visuals significantly enhance comprehension and decision-making, especially for non-Western users in critical fields like public health and international business. This paper provides three important contributions: (1) it shows that there are cultural barriers in AI visualization tools, (2) it gives a useful way to find and fix cultural bias in automated designs, and (3) it gives clear advice on how to develop AI-driven visual analytics that are more inclusive. This approach helps make sure that data is shared fairly and effectively in our globalized society by integrating AI automation with cultural understanding.