This study focuses on the application of deep learning techniques in the analysis of archaeological collections. The aim is to explore the potential of deep learning algorithms in artifact classification, pattern recognition, and reconstruction, while also discussing the challenges and considerations associated with their implementation. The study employs a literature review methodology, analyzing previous research conducted in this field. The findings reveal that deep learning algorithms offer promising capabilities in artifact classification, enabling automatic recognition and categorization based on visual attributes. They also provide powerful tools for pattern recognition, uncovering hidden connections between artifacts and enhancing our understanding of past societies. Additionally, deep learning techniques can revolutionize the reconstruction of archaeological sites and structures, allowing for detailed visualization and exploration of the built environment of the past. However, challenges such as the need for diverse training datasets, ethical considerations, and addressing potential biases and limitations of deep learning algorithms must be addressed. The study concludes by highlighting the potential of deep learning to unlock new insights and advance our understanding of the past.