The electrification of the mobility sector is vital to meet the targets for reducing greenhouse gas emissions. Besides battery-based mobility solutions, polymer electrolyte membrane fuel cells (PEMFCs) are a promising technology for electrifying drive trains, especially in heavy-duty applications, such as maritime or logistics. Bipolar plates, a key component of PEMFCs, can consist of two stainless-steel foils that must be welded to be gas-tight. In order to join the two metal foils, laser beam welding is the state-of-the-art technology. Current challenges include process instabilities at higher welding speeds, such as the humping effect, which can cause weld seam imperfections. Therefore, applying sensors for laser beam welding is a promising approach to monitor the welding process. AISI 316L foils were welded within the scope of this work with various process parameters using an adjustable ring mode laser beam source. Additionally, an optical microphone was used as a process monitoring system. By applying different parameter settings and due to the introduction of artificial faults, weld seam defects, such as a burn-through or a gap, were induced. After utilizing a noise reduction algorithm for the acoustic signals, numerous features in the time and frequency domain were extracted, with which multiple machine learning algorithms were trained and compared concerning their performance. A light gradient boosting machine was identified as a suitable machine learning model for the weld seam classification. Finally, a hyperparameter tuning was conducted, which resulted in a cross-validation accuracy of 94.78 %, depending on the quality categories considered.