This research investigates the impact of demographic characteristics, socio-economic indicators, and social media advertising on the online purchasing behavior of consumers. Employing advanced Deep Learning methodologies, the study aims to provide detailed insights into how these variables collectively influence consumer decision-making within digital marketplaces. By illuminating patterns in consumer behavior, this study aims to enable businesses to refine their marketing approaches to better cater to evolving consumer preferences, thereby fostering increased customer satisfaction and loyalty. Drawing upon a comprehensive dataset of online shoppers, correlations are analyzed, and predictive models are developed utilizing Deep Learning algorithms. The results reveal the considerable accuracy achieved by the Deep Neural Network (DNN) model, yielding a classification accuracy of 92%. In comparison, the Long Short-Term Memory (LSTM) model attained 84% accuracy, and the Gated Recurrent Unit (GRU) model achieved 87% accuracy. This study underscores the transformative potential of Deep Learning techniques in advancing our comprehension of consumer dynamics within the e-commerce realm, furnishing actionable insights for businesses seeking to optimize marketing strategies and enhance the online shopping experience.