Real-time and accurate road infrastructure monitoring is a major challenge in urban areas. Traditional methods, such as manual inspections by municipal staff or vehicular surveys using costly technologies like LiDAR or laser scanners, are prohibitively expensive, geographically constrained, and deployed infrequently. To address this, crowdsourcing has emerged as an effective approach for expanding both the coverage and frequency of infrastructure monitoring. Building on this concept, CrowPotChain introduces a novel platform that combines AI-driven pothole detection with secure blockchain-based report submission, ensuring tamper-proof and reliable crowdsourced data collection. The framework utilizes the YOLOv11s-seg model for semantic segmentation, combining convolutional neural networks (CNN) with transformer-based elements, which provides impressive detection metrics (precision: 0.889, recall: 0.894, mAP @ 0.5: 0.944). Every verified report includes geolocation, date/time, and pothole size, securely embedded in a proof-of-work (PoW) blockchain for verifiability and immutability. To examine the system’s performance, a benchmark was performed on four setups: no AI and no blockchain, AI only, blockchain only, and AI + blockchain, using batches of transactions from 10 to 100. The findings show that the no AI and no blockchain deployment provides the most rapid per-transaction time (approximately 0.010 s), followed by AI only (0.059–0.135 s), blockchain only (0.075–0.162 s), and AI + blockchain (0.145–0.696 s). Although blockchain does incur substantial overhead, its combination with AI can still serve response needs for civic infrastructure crowdsource reporting. Future development will add gamification, NFT, and IPFS to enhance participation, encourage reporting, and provide scalable decentralized storage.