<p>The rise in online selling of refurbished products through e-commerce websites presents opportunities for circular business models (CBMs), particularly in the smartphone category. However, creating an appropriate pricing strategy is crucial due to multiple costs, including logistics, refurbishment, and packaging for sustaining a CBM. This study gathered 3454 secondary data on refurbished smartphones from Indian e-commerce sellers, practicing the CBMs, recording features like release year, usage, memory, battery capacity, connectivity technology, weight, screen size, and price. Five features were engineered based on the data, and brand perception was analyzed to determine pricing. The top ten features were ranked based on feature contribution in classifier models trained as per the machine learning models, namely, Random Forest, Gradient Boosting, Logistic Regression, and Neural Network. Gradient boosting and Neural network emerged as the best predictive models, with release year, screen size, memory, and weight being key factors influencing pricing strategy based on seller’s perspective. The days used, battery, both cameras, release year, and weight are the top five important features for a refurbished smartphone with high brand perception. On the contrary, days used, battery, release year, front camera, and screen size are the top five important features for lower brand perception in refurbished smartphones. These two sets of features were identified based on the consumer’s perspective. Four important price-determinant features were identified for both high and lower brand perception, and they are release year, mAh/gm, ROM/mp, and screen size. The differentiating feature for high brand perception is the operating system, and for lower brand perception, it is the front camera. Finally, information from sellers and consumers is utilized for designing a pricing strategy for attracting consumers for sustainable CBM based on refurbished smartphones as a product.</p>