Potting compounds and coatings protect electronic components in harsh environments, requiring careful removal for recycling or repair. This study introduces the innovative use of YOLOv8-seg and Mask R-CNN to enhance the precision and efficiency of the laser cleaning process for PCBs (Printed Circuit Boards). These models are utilized for two primary tasks: real-time segmentation for laser cleaning guidance and post-cleaning surface quality assessment. Real-time segmentation adapts cleaning strategies based on PCB surface states such as 'Bare-Cu', 'Complete-Removal', 'Incomplete-Removal', etc. Quality assessment ensures high-quality, damage-free surfaces post-cleaning. Both models were trained on an augmented dataset to improve robustness. In the initial test dataset, YOLOv8-seg (l), known for its speed, achieved an mAP50 (seg) of 82.8% at 3.98 FPS, proving suitable for time-sensitive laser cleaning processes due to its speed and precision. Mask R-CNN (ResNet-50) reached an mAP50 (seg) of 84.097% at 1.52 FPS, fulfilling real-time requirements with high precision. Although their visualization segmentation results on the initial test dataset vary, both models successfully address the previously mentioned tasks. When tested on a new dataset with unseen patterns it was shown that YOLOv8-seg excels at generalizing to new patterns while Mask R-CNN performs less effectively. This study confirms YOLOv8-seg's effectiveness in real-time PCB monitoring during laser cleaning, boosting automation and efficiency in PCB recycling.