Within the complex domain of artificial intelligence (AI), the concept of knowledge representation assumes a crucial role, serving as a fundamental pillar that connects unprocessed data with significant interpretations. This chapter undertakes a thorough examination of knowledge representation, establishing connections with real-world situations. Commencing with a philosophical exposition on the fundamental nature of knowledge, we proceed to elucidate the hierarchical advancement from raw data to profound wisdom, underscoring the paramount importance of organized information in the facilitation of decision-making procedures. The central focus of this chapter explores a variety of methodologies utilized in the field of AI, encompassing semantic networks, frames, logic-based representations, and ontologies. The analysis of each method allows for the identification of its distinct capabilities and applications in the encoding, processing, and utilization of knowledge. As we traverse this terrain, the prevailing motif persists in the pursuit of facilitating machines to engage in reasoning, learning, and behaving in manners that resemble human cognition. This investigation not only elucidates the technical complexities of knowledge representation but also emphasizes its significant influence on the wider course of AI advancement.