Online pharmaceutical marketing is seeing a meteoric rise in attention as a potential use of artificial intelligence (AI), which has already revolutionized the healthcare business. This research delves into the factors influencing customer perception of online pharmacies, particularly concerning generative AI and ML algorithms. With their meteoric rise, online pharmacies have revolutionized the healthcare services business by making high-quality pharmaceuticals more accessible and convenient than ever before. Enhanced efficiency and precision in drug discovery can significantly improve the likelihood of obtaining successful drug approvals. Deep learning algorithms that assess real-world patient data enable the implementation of enhanced marketing tactics for personalized medicine applications. The convenience of e-medicines allows patients to transition from offline to online ordering methods, especially with the growing prevalence of internet apps. Multiple machine learning techniques have been suggested for predicting purchase intention due to their ability to successfully process complex and extensive datasets without human supervision. The robust data processing capabilities of deep learning have led to its recent remarkable achievements in several domains. Generative Adversarial Networks (GANs) using Multi-layer Perceptron (MLPs) and Long Short-Term Memories (LSTMs) as their discriminators and generators, respectively, are used to study the pattern of online pharmacy marketing’s behavioral aspects. An LSTM model constructs a generator to extract the data distributions of different items from the data and produce a prediction analysis of pharmacy marketing. By applying deep learning techniques, firms can employ recommender systems to forecast customers’ impressions. Recent advancements in deep neural networks have the immense potential to enhance the study of e-pharmacy greatly.