Oil and gas refineries plan maintenance schedules well in advance, and all business-critical applications are scheduled for use during this maintenance window to record activities status and track them for closure. As oil and gas refinery turnaround workloads increase on a need basis, they require scalable cloud solutions, particularly for their custom-built turnaround critical applications in Azure environments. Robust and adaptable resource management strategies are needed. We see frequent and severe consumption spikes related to refinery maintenance activities during turnaround periods that may cause significant performance challenges and result in operational delays and increased costs. The common problems of static scaling techniques are addressed in this study, which result in resource inefficiencies and decreased system responsiveness during these peak times that occur due to cold start latency of azure services, more often upscaling and downscaling of azure and latency delays while executing ansible playbooks. There is still a research gap in using Ansible for dynamic, real-time scaling of Azure Services that is suited to refinery turnaround workloads, even with the lot variety of automation tools available. Using a mixed-methods approach, this study develops and evaluates an Ansible-based automation framework that dynamically adjusts Azure App Service instances in response to real-time workload data. The approach combines infrastructure-as-code approaches with continuous monitoring to enable independent agentless, and adaptive automation workflows. Research findings indicate that by decreasing resource waste, enhancing application performance, and lowering related expenses during maintenance windows, the proposed framework greatly improves the efficiency of operations. Furthermore, automated and consistent configuration management minimizes human error and intervention, improving system reliability. These findings highlight the usefulness of optimizing cloud resources in refinery operations and point to increased adaptability across different sectors using identical workload fluctuations. By integrating cloud-based scaling and automation frameworks in industrial settings, the study adds to the body of knowledge by providing an adaptable framework for dynamic resource provisioning. In complex operational environments, it seems that combining Ansible integration analysing the latency metrics of Azure Services and enabling end to end scaling of all the services involved is an effective way to maintain cost-effectiveness and performance requirements.