Smart Allergy Management: Integrating ML into Immune Disorder Care

The study presents a Smart Allergy Management system that uses machine learning (ML) to transform immune disorder treatment. The existing healthcare system is based on delaying interventions and generalist therapies that lack accuracy. The proposed system combines ML algorithms for early identification with wearables for continuous tracking and environmental analysis. Patient education is improved with ML-powered applications and telehealth integration. Methodologically, Convolutional Neural Networks (CNN), AdaBoost, Random Forest, and Support Vector Machines (SVM) are used for early detection, tailored treatment plans, environmental monitoring, and telehealth integration. The results demonstrate enhanced early detection accuracy (94%), treatment adherence (87%), environmental alert response time (5 minutes), and telehealth efficiency (94%). Patients report a 30% reduction in allergy reactions, a better quality of life, and excellent satisfaction ($\mathbf{9 4 \%}$). The system’s versatility, ongoing learning, and ethical concerns guarantee a patient-centered system. Limitations include privacy problems, technological accessibility, and algorithm interpretability. Future study should address these concerns and investigate future technologies to improve system capabilities.