Pancreatic ductal adenocarcinoma (PDAC) patients, who often present with stage III or IV disease, face a dismal prognosis as the 5-year survival rate remains below 10%. Recent studies have revealed that CD4+ T, CD8+ T, and/or B cells in specific spatial arrangements relative to intratumoral regions correlate with clinical outcome for patients, but the complex functional states of those immune cell types remain to be incorporated into prognostic biomarker studies. Here, we developed an interpretable machine learning model to analyze the functional relationship between leukocyte-leukocyte or leukocyte-tumor cell spatial proximity, correlated with clinical outcome of 46 therapy-naïve PDAC patients following surgical resection. Using a multiplex immunohistochemistry imaging data set focused on profiling leukocyte functional status, our model identified features that distinguished patients in the fourth quartile from those in the first quartile of survival. The top ranked important features identified by our model, all of which were positive prognostic stratifiers, included CD4 T helper cell frequency among CD45+ immune cells, frequency of Granzyme B-positivity among CD4 and CD8 T cells, as well as the frequency of PD-1 positivity among CD8 T cells. The spatial proximity of CD4 T- to B cells, and between CD8 T cells and epithelial cells, were also identified as important prognostic features. While spatial proximity features provided valuable prognostic information, the best model required both spatial and phenotypic information about tumor infiltrating leukocytes. Our analysis links the immune microenvironment of PDAC tumors to outcome of patients, thus identifying features associated with more progressive disease.