In past years, the rapid progress of artificial intelligence has captured the interest of numerous researchers in this field. Image captioning, which involves automatically generating human-like descriptions based on the content of an image, has emerged as an intriguing and challenging endeavor. This paper explores the field of picture captioning with a particular emphasis on two fundamental components: attention mechanisms and Long Short-Term Memory (LSTM) networks. We first examine the advantages and disadvantages of LSTM and then go into great detail on attention mechanisms. The benefits and difficulties of using widely used datasets are covered in detail. This abstract provides readers with a concise overview of the complex interactions between LSTMs, attention mechanisms, and dataset dynamics in current picture captioning research. This paper provides an overarching overview of the various methods employed in image captioning and places a particular emphasis on the attention mechanism. Additionally, we delve into a discussion of the merits and limitations associated with these methods. We also furnish information about the commonly used datasets and the evaluation criteria that are prevalent in this field.