The rapid proliferation of hyper-connected digital platforms has significantly reshaped fashion consumption patterns through artificial intelligence–driven algorithmic recommendation systems. Platforms such as Instagram, Myntra, Amazon, and Flipkart leverage real-time data analytics to curate personalised apparel suggestions, influencing consumer decision-making processes. Reports suggest India’s online lifestyle market—of which fashion/apparel is a major segment—is projected to grow from $16–17 billion today to $40–45 billion by 2028, with Gen Z contributing disproportionately to this rise. Generation Z represents one of the most influential consumer cohorts in the global fashion industry. In India alone, Gen Z comprises approximately 377 million individuals and contributes nearly 43% of total consumer spending, with significant allocation toward fashion, footwear, and lifestyle products (IndiaTimes, 2025). Industry reports indicate that nearly 70% of online purchase decisions are influenced by algorithmic recommendations, while Generation Z and Generation Alpha are projected to contribute approximately 40% of global fashion spending by 2035, underscoring their growing economic significance. Furthermore, surveys reveal that over 60% of Generation Z consumers admit to making impulse purchases online, particularly in fashion categories, highlighting their susceptibility to digitally mediated stimuli. Against this backdrop, the present study investigates the dual role of algorithmic recommendations in stimulating impulsive apparel consumption and shaping consumer resilience among Generation Z women. Drawing on the Stimulus–Organism–Response (S-O-R) framework and resilience theory, algorithmic personalisation is conceptualised as the stimulus, emotional arousal and hedonic motivation as organismic responses, and impulsive buying as the behavioural outcome, with consumer resilience—reflected in self-regulation and digital well-being—acting as a moderating construct. Primary data will be collected through a structured questionnaire and analysed using Statistical tools incorporating Confirmatory Factor Analysis (CFA) to ensure construct validity and reliability. By integrating statistical market evidence with behavioural theory, this study aims to contribute to digital consumer research by explaining how resilience mechanisms can mitigate algorithm-driven impulsive consumption in hyper-connected fashion ecosystems and inform responsible AI-driven marketing strategies.