Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers, marked by complex interactions between tumor and stromal cells and significant resistance to treatment. Understanding the cellular and molecular heterogeneity within the PDAC tumor microenvironment is crucial for developing more targeted therapeutic strategies. Spatial transcriptomics offers a powerful approach to capture cell-to-cell interactions within the tumor microenvironment, revealing tissue architecture and providing insights into the spatial organization of cellular populations. Methods: Spatial transcriptomics was performed on three PDAC samples from a single patient to explore cellular and genetic complexities within the tumor microenvironment. Cells were manually annotated; however, some cells remained unclassified based on stringent criteria. To address this, an XGBoost machine learning classifier was developed, utilizing differentially expressed genes from tumor cells and fibroblasts as classification features. After sample integration, the number of clusters for tumor cells and fibroblasts was calculated, resulting in the identification of three tumor clusters (T1, T2, T3) and three fibroblast clusters (F1, F2, F3). The spatial organization of these clusters was further investigated using the BuildNicheAssay function, which applies a KNN algorithm to define the local neighborhood of each cell, followed by K-means clustering to categorize cells into three spatial niches based on prior knowledge. Results: Three distinct cell niches were identified: T1F2 (the proliferating hub), T2F3 (the metabolic battery), and T3F1 (the adaptive core). Each niche displayed unique functional properties. The T1F2 niche, containing proliferating tumor cells supported by fibroblasts, was associated with enhanced tumor growth and angiogenesis. The T2F3 niche demonstrated considerable metabolic plasticity, supporting energy demands. The T3F1 niche was characterized by epithelial-mesenchymal transition (EMT)-driven tumor cells interacting with fibroblasts, coupled with WNT/β-catenin and hedgehog signaling pathways, providing insights into PDAC invasion and metastasis mechanisms. Conclusion: Our study reveals a niche-based model of PDAC progression, where specialized microenvironments contribute distinct functionalities to tumor growth and adaptation. This model enhances our understanding of PDAC heterogeneity, offering insights that may guide the development of more targeted and effective therapies.