The emergence of the Generative Pre-trained Transformer (GPT) signifies a substantial advancement in the realm of Natural Language Processing (NLP), pushing us toward the advance of machines that can well understand and communicate in a way very similar to human language. Basically, any GPT is deep-rooted in the transformer architecture, which is a sophisticated neural network tailored for tasks pertaining to natural language processing and has garnered significant acclaim for its exceptional performance in handling language-related tasks and its adeptness in engaging in meaningful conversations. This has led to widespread recognition and adoption of GPT models in both research and industrial spheres, establishing them as pivotal and effective tools for natural language processing and allied fields. Consequently, the extensive utilization and success of GPT models serve as the primary impetus for undertaking the comprehensive review presented in this paper. This comprehensive review conducts an in-depth exploration of GPT, encompassing its architectural components, operational procedures, training methodologies, underlying technologies, and its impact on various practical applications. Furthermore, the paper explores the potential obstacles and limitations of GPT, exploring possible strategies and future directions to address these challenges. In summary, the aim of this paper is to enhance the understanding of GPT. It seeks to empower and influence us in a variety of roles. On the other hand, it also addresses emerging challenges and offers proactive solutions.