Dynamic Resource Optimisation for AI-Powered Data Analytics in Cloud Environments

To achieve scalability, cost-effectiveness, and performance dependability, Artificial Intelligence (AI)-driven applications in cloud computing have rapidly expanded, thereby demanding efficient and adaptive resource utilization. The dynamic computing and storage demands of data-intensive AI applications often exceed the capacity of static allocation systems, resulting in inefficiencies and service delays. This research introduces an AI-powered data analytics Dynamic Resource Optimisation Framework (DROF) to handle cloud workload variability and energy overhead. To anticipate resource needs and optimise allocation in real time, the proposed DROF uses reinforcement learning and deep neural resource mapping. DROF balances cost and performance by dynamically scaling resources based on task priority, workload size, and system condition. DROF decreases latency by 28%, resource use by 32%, and energy consumption by 19% compared to standard approaches in cloud simulation platforms. The results show that the suggested method improves operational efficiency and promotes data-driven AI analytics cloud architecture. DROF optimises performance and resource distribution in current cloud systems with its scalable, intelligent, and energy-aware solution.