The insurance industry increasingly relies on artificial intelligence to drive operational efficiency and customer satisfaction. This study will explore the transformative potential of AI-driven image recognition for damage assessment in auto and home insurance claims. Traditional damage assessment methods are usually performed through manual inspections, which are time-consuming, costly, and inconsistent. Such advanced AI models, through which computer vision and machine-learning techniques are exercised, bring automation to damage detection and evaluation more precisely, rapidly, and cost-effectively. Now, insurers can evaluate claims more fairly and precisely with the help of AI by analyzing high-resolution images from drones, satellite imaging, and smartphone cameras. The article reviews state-of-the-art damage detection methodologies, including 3D point cloud analysis, infrared thermography, and pattern recognition, to prove their effectiveness in real-world applications. This also presents real-world applications and case studies in disaster damage assessment and fraud detection. This will result in the optimization of the claims process, reducing operational costs and increasing policyholder trust through AI, which will be a tremendous stride in the insurance industry.

Artificial Intelligence, Image Recognition, Damage Assessment, Auto Insurance, Home Insurance, Claims Processing, Machine Learning, Computer Vision, Fraud Detection, Remote Sensing, Automated Inspections.