Corporate sustainability has evolved from a strategic initiative to a fundamental business imperative, prompting firms to explore new ways to understand and influence customers' purchasing decisions regarding greener products as they strive to maintain their edge in increasingly digital markets where expectations are evolving. This chapter demonstrates how Nigerian companies can utilize artificial intelligence in conjunction with Internet of Things tools to sift through complex streams of customer data in real-time and align their green actions with what shoppers perceive as environmentally friendly. The study employs stratified random sampling across urban shopping centers, suburban retail outlets, and online-to-offline hybrid stores in Nigeria, representing diverse consumer demographics and shopping behaviors. Data collection encompasses retail kiosks, shopping apps, home sensors, and wearables over twelve months. The authors apply machine-learning models, natural language processing, sentiment scoring, predictive dashboards, and clustering techniques to map customer preferences, purchasing patterns, and green program participation. Data analysis combines quantitative analytics with qualitative sentiment analysis, while environmental impact data is collected through IoT sensors measuring energy consumption, waste generation, and carbon footprint metrics. Businesses implementing these insights demonstrate a 25 to 40% increase in loyalty while reducing their ecological footprint through tailored green messages, smarter product suggestions, and targeted eco-marketing aligned with shoppers' values. The article contributes to sustainable change literature by demonstrating that insight-driven engagement drives profit while advancing environmental goals. Results underscore that firms must incorporate data-driven analysis into their sustainability plans to gain actionable insights and develop customer strategies that boost profits while enhancing ecological responsibility.