Generative AI has transformed content creation across various domains, but it has also raised concerns about cultural biases in AI-generated outputs. As AI models are trained on vast amounts of data, they can inadvertently reflect or amplify the cultural contexts inherent in such data. It is crucial to understand the cultural implications of generative AI to ensure that AI-generated content aligns with the values of different cultures and avoids reinforcing stereotypes. In this study, AI-generated texts from diverse cultural perspectives are analyzed, using techniques such as sentiment analysis and K-means clustering. Texts from Western, Eastern, Middle Eastern, and African contexts were processed through sentiment analysis to assess the emotional tone and categorized into clusters based on common themes using K-means clustering. Principal Component Analysis (PCA) was applied to reduce dimensionality and visualize the clustering results. The findings revealed two distinct clusters, each associated with unique cultural themes. Cluster 1 highlighted technology and future-oriented discussions in Eastern and Western texts, while Cluster 2 emphasized sustainability and development in African texts. The sentiment analysis indicated a generally positive tone in Western texts compared to neutral or mixed sentiments in texts from other regions. These results underscore the importance of recognizing cultural biases in generative AI and developing models that produce more culturally aware and inclusive content. By understanding the implications of AI-generated content, developers and policymakers can ensure that AI technologies serve diverse cultural needs without perpetuating stereotypes or biases.