Abstract The rise of e-commerce has transformed consumer behaviour, making the prediction of buying intentions critical for digital marketing strategy. This study integrates Artificial Intelligence (AI) and advanced statistical methods to examine how machine learning (ML) models and Structural Equation Modelling (SEM) can jointly predict consumer purchase intentions. Using a dataset of consumer reviews and behavioural signals from leading e-commerce platforms, we employ sentiment analysis, TF-IDF text vectorization, and predictive modelling across Logistic Regression, Random Forest, XGBoost, and Neural Networks. We also propose and test a SEM framework linking consumer trust, product quality perception, and sentiment polarity to buying intentions. Results highlight that ensemble ML models outperform linear classifiers, while SEM provides theoretical grounding by validating latent constructs. The hybrid SEM-ML framework demonstrates both predictive accuracy and theoretical rigor. Findings offer practical insights for e-commerce managers on optimizing customer engagement strategies. To improve theoretical clarity and robustness we explicitly anc
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