Type 2 diabetes mellitus (T2DM) self-management depends on interacting behavioral, clinical, problem-solving, and psychosocial factors that are difficult to represent using rigid categorical models. This study presents FES-T2DSC (Fuzzy Expert System for Type 2 Diabetes Self-Care), an application-specific fuzzy expert system for multidimensional self-care assessment in adults with T2DM. The system comprises four type-1 Mamdani fuzzy inference models—Basic Self-Care, Diabetes Self-Management, Problem-Solving Capacity, and Psychosocial Status—with explicit membership functions and linguistic IF–THEN rules. Development used records from 27 adults with T2DM recruited in an outpatient municipal setting in Veracruz, Mexico; 93 synthetic configurations from the original analysis are retained only as historical descriptive context, not validation evidence. The architecture preserves separate domain-level outputs and makes the encoded rule bases and membership functions inspectable. Quantitative evidence consists of an internal, non-independent archival reanalysis of archived outputs; the currently available FIS files are not established as their generators, and no external clinical validation was performed. FES-T2DSC therefore provides an integrative, rule-based architecture for multidimensional diabetes self-care assessment, while larger independent cohorts, reproducible execution pipelines, and prospective evaluation are required before external performance, generalizability, or clinical utility can be established.