Kumara P.S.D.NDahanayake Y.D.N.PJayalath U.W.T.P.Ranawaka T.ASamarakoon, UTissera, W2026-10-072025-12-09P. S. D. N. Kumara, Y. D. N. P. Dahanayake, U. W. T. P. Jayalath, T. A. Ranawaka, U. Samarakoon and W. Tissera, "Personalized AI System for Maternal Nutrition and Exercise," 2025 7th International Conference on Advancements in Computing (ICAC), Colombo, Sri Lanka, 2025, pp. 1-6, doi: 10.1109/ICAC69156.2025.11361494.979-833156222-9https://rda.sliit.lk/handle/123456789/5343This work presents a personalized AI system for maternal wellness that generates two coordinated, daily outputs for pregnant users: culturally adapted meal plans and risk-aware exercise prescriptions. In its nutrition module, the combination of a library of curated local dishes and trimester-specific energy corridors with condition-aware rules (diabetes/gestational diabetes, anemia, hypertension, allergies) highlights iron, folate, protein, and calcium instead of full nutrient panels. The exercise module comprises a rules-first risk screen (Low/High) corroborated by a calibrated gradient-boosted classifier, followed by a template-based planner that renders day-level sessions with safety guardrails, trimester adaptations, and video guidance. Evaluation on de-identified datasets (2,000 meal profiles; 970 risk records; 1,934 exercise prescriptions) showed high nutritional adequacy and reliable safety enforcement: daily energy corridors were met on 94.7% of days and ≥3/4 essential nutrients on 91.3%, with 0 allergen violations. The risk classifier achieved AUROC 0.86, accuracy 0.82, sensitivity 0.79, and specificity 0.84. The exercise recommender successfully enforced trimester- and risk-based guardrails, with all plans passing safety checks. These findings, from a study focused on Sri Lanka, suggest that an integrated, objective and culturally based method can provide personalized, safe guidance daily in resource-constrained environments, and serve as a basis for future clinical validation and predictive modeling.enexercise prescriptionmaternal healthnutritionpersonalizationpregnancyConference Paperdoi: 10.1109/ICAC69156.2025.11361494