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Volunteer organizations and Non-Governmental Organizations (NGOs) play a crucial role in promoting social welfare activities such as environmental protection, disaster relief, community service, and education. However, verifying the authenticity of volunteer-reported activities remains a significant challenge. In many existing systems, volunteers upload images as proof of task completion, but there is no reliable mechanism to determine whether these images are genuine, duplicated, sourced from the internet, or generated using artificial intelligence tools. This lack of verification reduces transparency and trust in volunteer management systems. This research proposes an AI-Based Volunteer Work Verification and Reward Management System that automatically verifies images submitted by volunteers. The system integrates multiple techniques including Convolutional Neural Networks (CNN) for AI-generated image detection, Perceptual Hashing for duplicate image detection, Structural Similarity Index (SSIM) for image comparison, Time Series Analysis for monitoring volunteer activity patterns, a ranking algorithm for generating volunteer leaderboards, and a rule-based system for reward classification. The proposed system evaluates uploaded images through multiple verification stages before assigning points to volunteers. Based on accumulated points, volunteers are awarded achievement badges such as Bronze, Silver, and Gold, along with reward incentives. By ensuring authenticity and transparency in volunteer activity reporting, the system encourages genuine participation and enhances accountability within NGO-based volunteer management platforms.