Additive manufacturing (AM) is a novel fabrication technique which enables production of very complex designs that are not feasible through conventional manufacturing techniques. However, one major barrier against broader adoption of additive manufacturing processes is concerned with the quality of the final products, which can be measured as presence of internal defects, such as pores and cracks, affecting the mechanical properties of the fabricated parts. In this paper, a data-driven methodology is proposed to predict the size and location of porosities based on in-situ process signatures, i.e. thermal history. Size as well as location of pores highly affect the resulted fatigue life where near-surface and large pores, compared to inner or small pores, significantly reduces the fatigue life. Therefore, building a model to predict the porosity size and location will pave the way toward building an in-situ prediction model for fatigue life which would drastically influence the additive manufacturing community. The proposed model consists of two phases: in Phase I, a model is established to predict the occurrence and location of small and large pores based on the thermal history; and subsequently, a fatigue model is trained in Phase II to predict the fatigue life based on porosity features predicted from Phase I. The model proposed in Phase I is validated using a thin wall fabricated by a direct laser deposition process and the Phase II model is validated based on fatigue life simulations. Both models provide promising results that can be further studied for functional outcomes.