Modern corporate tactics depend critically on customer segmentation, which helps businesses to customize their marketing campaigns to different client segments. As per Springer style, both city and country names must be present in the affiliations. Accordingly, we have inserted the city name “Lodz” in the affiliation. Please check and confirm if the inserted city name is correct. If not, please provide us with the correct city name. Often depending on specified criteria, traditional segmentation techniques may ignore underlying trends in consumer behavior. This work investigates the use of un-supervised deep learning methods to improve consumer segmentation, therefore providing a more dynamic and data-driven method. Important methods like generative models, autoencoders, and deep clustering algorithms are reviewed with an eye toward their capacity to find latent trends in big data. Reevaluating real-world applications—including case studies in the retail and financial services sectors—the research also shows how well these methods enhance segmentation accuracy, scalability, and customisation. Although unsupervised deep learning presents many benefits, the research also covers issues including data quality, model interpretability, and integration with current systems.