The rise of predictive commerce, driven by big data, machine learning, and advanced analytics, is transforming the global retail landscape by forecasting consumer demand, personalizing shopping experiences, and reshaping competitive dynamics. While large e-commerce platforms benefit from these technologies, small retailers in emerging economies face growing challenges due to limited resources, technological constraints, and loss of traditional consumer trust. This study investigates the disruptive impact of predictive commerce on small retailers in Tamil Nadu, India, and explores adaptation strategies that support survival and resilience. Using a mixed-method approach with survey data from 60 respondents, the study applies Exploratory Factor Analysis (EFA) to examine four key constructs: predictive commerce, disruption, adaptation strategies, and retailer outcomes. The results confirm the robustness of the conceptual framework, with strong factor loadings and acceptable measures of sampling adequacy (KMO = 0.724; Bartlett’s Test, p < 0.001). Findings reveal that predictive commerce creates economic, operational, and social disruptions, but that adaptation strategies such as digital tool adoption, hybrid business models, platform collaboration, and technology training significantly mediate negative effects. The study contributes theoretically by extending resilience and resource-based perspectives to digital retail disruption, and practically by offering actionable insights for policymakers and platforms to design inclusive ecosystems. Overall, the research highlights that small retailers’ survival depends not on resisting predictive commerce but on strategically integrating digital opportunities to remain competitive and resilient in a rapidly evolving retail environment.