In the age of digital globalization, online English learning platforms have turned out to be must in eradicating linguistic and cultural gaps of virtual learners. To systemically uncover and interpret cross cultural communication pattern in such environment, this study introduces a big data analytics framework. The proposed methodology is designed into four main phases such as data collection and preprocessing, feature engineering and analysis, pattern detection and modelling, and interpretation and visualization. A wide number of platforms including forums, mobile apps, and digital classrooms are then automatically collected data, filtered and processed into language translation, audio/video transcription and anonymization. Additionally, the framework addresses cultural variations and ensures data privacy and ethical compliance. In order to accommodate the differences in cultural variations in communication style, emotional expression, and collaborative behavior, features are extracted based on sociolinguistic and sentiment. The extracted features enable detailed modeling of learner engagement patterns and emotional dynamics across different cultural cohorts. To model communication trends and discover unique time-based interaction patterns taking place culturally, the advanced machine learning and deep learning techniques are leveraged including the K-Means clustering, Support Vector Machines (SVM) and Long Short-term Memory (LSTM) network. Then, they are visualized with heatmaps and network graphs for both the design of an adaptive course and the personalized learning. It enables the culture responsive digital education development as well as the improving of learner engagement and intercultural understanding in online global learning environments.