Ovarian cancer exhibits marked heterogeneity within its complex tumor microenvironment, driving progression, metastasis, and therapeutic resistance. This study presents a novel integration of spatial transcriptomics with single-cell RNA sequencing to comprehensively characterize the molecular landscape of ovarian cancer tissues. Employing the Leiden clustering algorithm at an optimal resolution of 0.7, we identified 13 distinct cellular subpopulations with unique transcriptional signatures. Pseudotime trajectory analysis revealed dynamic gene expression patterns associated with disease progression. Notably, FOSB, AMOTL2, and SLCO4A1 showed progressive upregulation during cellular differentiation, strongly implicating them in tumor cell motility and metastatic potential. Conversely, PLEK, CFB, and ADGRB1 maintained stable expression patterns, suggesting critical roles in maintaining cellular homeostasis and signal transduction. We observed significant downregulation of mitochondrial genes (particularly MT-CO2) and extracellular matrix components (such as COL1A2) in later differentiation stages, highlighting their importance in early oncogenic processes. Functional enrichment analysis connected these gene clusters to key biological pathways including extracellular matrix remodeling (COL family), mitochondrial energy metabolism (MT-CO2), and immune response modulation (CD163, PLEK). Our findings not only provide unprecedented insights into the molecular architecture of ovarian cancer but also identify several promising biomarkers and potential therapeutic targets. This integrative spatial-temporal approach establishes a robust framework for understanding the complex interplay within the tumor microenvironment and offers new strategic directions for developing personalized interventions in ovarian cancer treatment.