This thesis explores two main topics: the effects of the temperature on several Quantum Chromodynamics mesonic observables, with a concrete focus on the tem-perature dependence of the mesonic mass spectrum, and numerical spectral recon-struction of lattice correlation functions employing deep neural networks. In the first two chapters, a brief introduction to standard lattice Quantum Chromodynamics and non-zero temperature field theory is provided. Using the tools presented in the intro-ductory chapters, a complete spectroscopy analysis of the temperature dependence of several mesonic ground state masses is developed. From this study, novel results in the restoration of chiral symmetry as a function of the temperature are obtained by studying the degree of degeneracy between the ρ(770) and a1(1260) states. Ad-ditionally, a complete study of the thermal effects affecting the mesonic D(s)-sector below the pseudocritical temperature of the system is provided. A self-contained chapter discussing the pion velocity in the medium is also included in the document. The pion velocity is estimated as a function of the temperature using non-zero tem-perature lattice Quantum Chromodynamics. In addition, after providing a detailed introduction to the field of neural networks, their application to numerical spectral reconstruction is studied. A simple implementation in which deep neural networks are applied to numerical spectral reconstruction is tested in order to explore its limits and applicability
