This paper presents a comprehensive network-based analysis of global trade dynamics with a focus on identifying structural patterns, key trade hubs and anomalies in international commerce from 2018 to 2022. We model the global trade network as a directed, weighted graph where countries are nodes and trade relations are weighted edges by value of transactions and employ six community detection algorithms-Louvain, Girvan-Newman, Label Propagation, Connected Components, LFM and SLPA-to uncover trade blocs, with Louvain achieving the highest modularity score of 0.4185. Trade hub detection was performed using PageRank centrality and K-Core decomposition which reveals 44 countries as persistent trade hubs, present in the maximum core over three consecutive years. Anomaly detection was conducted using three separate approaches, namely the Interquartile Range (IQR), Z-score-based temporal analysis and structural embedding-based Isolation Forest. Temporal patterns are analyzed to identify the emergence of trade relations and changing centrality of nations. The results highlight core-periphery structures, persistent hubs and temporal disruptions-providing insights into the evolving topology and resilience of global trade networks. This EDA is the backbone for future prescriptive and predictive modeling in the analysis of world trade.