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Our study comprehensively explores the complex role of programed cell death (PCD) mechanisms in PDAC development, examining 18 distinct PCD pathways and their genetic underpinnings. Using an advanced machine learning framework incorporating 429 algorithmic variations, we have developed an innovative PCD\u2010based molecular signature that demonstrates robust prognostic capabilities. This signature exhibits superior performance across diverse patient cohorts, significantly outperforming traditional clinicopathological indicators. Through integrated pathway analysis, we revealed that high\u2010risk patients show distinct activation of oncogenic pathways and significant alterations in the tumor immune microenvironment. These alterations include reduced infiltration of cytotoxic T lymphocytes and increased levels of immunosuppressive regulatory T cells (Tregs). Furthermore, leveraging the TISCH (Tumor Immune Single Cell Hub) database, we conducted detailed single\u2010cell expression profiling of our signature genes across different cell populations within the tumor microenvironment (TME). This analysis uncovered cell\u2010type\u2010specific expression patterns of key PCD\u2010related genes. Our results highlight the critical involvement of PCD in PDAC progression and introduce a promising tool for clinical risk stratification. The integration of bulk and single\u2010cell transcriptomic analyses not only validates our molecular signature but also reveals potential cellular targets for therapeutic intervention. 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