The rapid growth of EOL and used products has intensified the need for circular manufacturing strategies that enable reuse and remanufacturing of products. One of the most critical challenges in this context is the visual inspection of EOL/used products to determine reusability. Traditional approaches rely either on manual inspection, which is often slow, inconsistent, and dependent on individual judgment, or on automated inspection systems based on rule-based image processing, which lack adaptability to new defect types, new products, and changing environmental conditions. This thesis addresses these challenges in the electronics domain by developing an AI-based classification method to classify burnt printed circuit boards (PCBs) from reusable PCBs.