Allergic diseases are increasing worldwide, underscoring the need for innovative management strategies. Wearable and connected technologies combined with artificial intelligence (AI) can support real-time monitoring, personalized alerts, and proactive interventions. This review summarizes AI-enabled tools for allergy care spanning physiologic signals, environmental exposures (e.g., pollutant proxies such as particulates/VOCs), and patient behaviors, as well as connected medication-adherence technologies (e.g., digital inhalers) that integrate with the same analytics workflows. We also outline predictive algorithms that forecast exacerbations and briefly review therapeutic devices. Reported benefits include earlier warning of clinical deterioration, improved adherence/technique, and opportunities for tailored management. However, important limitations remain around data accuracy and reliability, user adoption, workflow integration, equity/fairness, privacy/cybersecurity, and evolving regulatory pathways. Critically, most devices and algorithms reviewed are investigational or early-phase, with evidence dominated by feasibility or short-term studies, and only a few show improvements in patient-centered outcomes in prospective trials. Realizing clinical value will require outcomes-focused validation (including external/pragmatic studies), safeguards for privacy and security, attention to bias and subgroup performance, and implementation models that fit clinical workflows and reimbursement. With these conditions met, AI-driven wearable and connected technologies could enable more proactive, personalized allergy care.