Metal laser powder bed fusion (L-BPF) technology is one of the most common and evolved additive manufacturing technologies to fabricate metal components. However, the control of defects generated during the SLM process remains an essential technological challenge for its implementation in production lines. In this work, based on the combination of eddy current testing (ECT) and machine learning (ML) approach, we propose a methodology allowing the in-situ monitoring of LPBF process porosity defects of Ti-6AL-4V components. The present empirical approach is achieved by setting up trained AI algorithms for the in-situ detection of porosity defects generated during the part fabrication. The algorithms are fed with data collected layer by layer using a specific experimental set up composed of an ECT system mounted on the machine recoater of the SLM machine. Comparison between predicted and experimental outcomes shows the effectiveness of the proposed framework which allows the prediction of porosity defects layer by layer with a mean absolute error (MAE) of 0.1% for CNN2D algorithm and 0.11% for LSTM one. The framework developed in this study can be effectively applied to quality control in additive manufacturing.