Video captioning (VC) is a vital task in multimedia understanding. VC presents significant challenges in the realm of deep learning due to the complexity of processing video data and the need for large-scale annotated datasets. Despite the importance of VC, accessible resources and explanations in this domain are often limited. In this paper, we conduct a comprehensive review of VC literature, encompassing papers, models, training techniques, evaluation metrics, and real-world applications. By bridging the gap between complex deep learning concepts and accessible explanations, we aim to highlight state-of-the-art methodologies, challenges, and opportunities in VC research. Our review not only highlights the technological advancements but also discusses the practical implications and potential impact of improved VC systems on various applications, including content accessibility, video search, and summarization. We hope that this work will serve as a valuable resource for researchers and practitioners, encouraging further exploration and development in VC.