Wind turbine blades are prone to damage due to various factors such as moisture absorption, fatigue, wind gusts, and lightning strikes. Detecting this damage is crucial to preventing catastrophic failures that could compromise the entire turbine system. This study aims to reduce maintenance costs, particularly for offshore wind turbines, by leveraging data-driven approaches for failure analysis and diagnosis. While supervisory control and data acquisition (SCADA) systems and structural health monitoring (SHM) techniques have been widely used for blade condition assessment, they often require physical inspections for damage verification. To address this limitation, this research develops an automated methodology using unmanned aerial vehicles (UAVs) for blade inspection and captured images are processed using convolutional neural networks (CNNs) for real-time damage detection. The detected damage is then analyzed through finite element method (FEM) simulations to assess its structural impact. By integrating deep learning with FEM analysis, this approach enhances wind turbine maintenance efficiency, minimizing costs and reducing the need for manual inspections. Notable findings of the study include a model trained to achieve a mean average precision (mAP) of 99.5% for mAP@50 and 89.8% for mAP@50:95, with a successful case study demonstrating damage detection and structural analysis through the integrated algorithm.