The United States carries the highest chronic disease burden among high-income countries, yet preventive services reach the populations that need them least often. This study examined these relationships and assessed whether survey-based predictors could support individual-level classification and inform preventive-healthcare policy. Data were drawn from the 2024 National Health Interview Survey Sample Adult File (N = 32,629; complete-case analytic samples of 29,110–29,194). A four-stage design used multinomial and binary logistic regression to examine two healthcare utilization outcomes and five self-reported chronic conditions: hypertension, diabetes, cardiovascular disease, high cholesterol, and cancer. Extended models incorporated visit recency and wellness-check status, while Random Forest classifiers provided a complementary assessment of predictive performance. Models were evaluated on held-out test data against majority-class baselines. Continuity of care and visit recency consistently associated with utilization and reported diagnoses, while their associations with insurance coverage weakened after accounting for visit recency, with racial and educational associations varying by condition. Classification accuracy exceeded majority-class baselines for hypertension (+11.3 percentage points) and high cholesterol (+3.4 percentage points), but not for diabetes, cardiovascular disease, or cancer. These findings demonstrate the value of survey-based analysis for identifying population-level patterns in healthcare access and diagnosed disease, while highlighting limits to individual-level prediction using the selected variables. Policy implications include strengthening continuity of care and investigating whether lower reported diagnosis rates among populations facing access barriers reflect unmet screening needs. Cross-sectional, self-reported data limit causal inference and cannot establish undiagnosed disease prevalence.