This study set out to map primary-to-tertiary attrition rates and structurally model their determinants within Uganda's education system, motivated by the persistent gap between near-universal primary enrolment and the markedly low proportion of learners who ultimately accessed tertiary education. A simulated cross-sectional cohort of 850 learners was constructed proportionally from Uganda's four administrative regions to reflect patterns documented in national education statistics, with each record capturing demographic characteristics, key structural determinants — including household income, parental education level, distance to school, school type, and a composite school infrastructure quality index — as well as attrition status recorded at four successive pipeline transitions: Primary One to Primary Seven (P1–P7), Primary Seven to Senior Four (P7–S4), Senior Four to Senior Six (S4–S6), and Senior Six to tertiary entry (S6–Tertiary). Data were analysed through univariate descriptive statistics, bivariate chi-square tests, independent-samples t-tests, and point-biserial correlations, before structural equation modelling (SEM) was applied with maximum likelihood estimation and robust standard errors, specifying Household Socioeconomic Capital, School Infrastructure Quality, and Geographic Accessibility as latent predictors of attrition. Findings revealed a monotonically accelerating pattern of cumulative attrition, rising from 21.4% at the P1–P7 transition to 38.7% at P7– S4, 46.2% at S4–S6, and 61.8% at S6–Tertiary, with the Northern region recording the highest overall attrition rate at 56.3%. Bivariate analysis confirmed statistically significant associations between attrition and all structural determinants examined (p < 0.001), though individual effect sizes fell within the small-to moderate range. The structural model demonstrated good fit (CFI = 0.954, TLI = 0.941, RMSEA = 0.046, SRMR = 0.038) and identified Household Socioeconomic Capital as the strongest direct determinant of attrition (β = −0.42), followed by School Infrastructure Quality (β = −0.31) and Geographic Accessibility (β = 0.27 direct; β = 0.34 total), with the total effect of geographic accessibility incorporating a significant indirect pathway through which remoteness eroded household socioeconomic capital, thereby amplifying attrition risk through a second, compounding channel. The study concluded that learner attrition across Uganda's education pipeline was structurally produced by the interlocking influence of household poverty, school infrastructure deficits, and geographic remoteness, and recommended that effective policy responses address these determinants simultaneously through targeted household financial support, infrastructure investment in remote government schools, and expanded geographic accessibility interventions such as school transport schemes, satellite campuses, and all-weather road development.