AI Assisted Resource Optimization in Hybrid Mobile Applications Using Cloud and On Device Intelligence

Hybrid mobile applications enable cross platform development across Android and iOS using a shared codebase, but they often face performance and efficiency challenges when integrating advanced intelligence features. The growing adoption of Generative Artificial Intelligence in mobile applications has intensified these challenges due to high computational demand, variable network conditions, and limited device resources. Existing hybrid mobile applications typically rely on static decision logic to invoke cloud based AI services, resulting in inefficient resource utilization, increased latency, and inconsistent user experience. This paper proposes an AI assisted resource optimization framework for hybrid mobile applications that dynamically balances execution between on device intelligence and cloud based AI services. The proposed framework introduces a lightweight decision engine that continuously evaluates device state, network conditions, workload complexity, and historical usage patterns to determine optimal execution strategies. Instead of relying on fixed thresholds, the system applies machine learning based prediction to decide when AI tasks should be executed locally, offloaded to the cloud, or served from cache. The framework is designed to integrate seamlessly with hybrid mobile development frameworks without modifying application logic. A reference implementation demonstrates how adaptive execution improves responsiveness and reduces unnecessary cloud invocations under varying network and device conditions. Experimental evaluation using representative AI driven interactions shows improved latency stability, reduced network usage, and better energy efficiency compared to static cloud first approaches. The results demonstrate that incorporating AI assisted optimization into hybrid mobile applications enables more efficient and scalable integration of Generative AI capabilities. This work provides practical insights for developers and researchers seeking to build intelligent mobile systems that adapt dynamically to real world operating conditions.