The Advanced LIGO/Virgo observation campaigns have revealed the rich and diverse physics of binary neutron star (BNS) and binary black hole mergers. In 2017, the simultaneous discovery of GWs and electromagnetic (EM) counterparts from a BNS merger provided an exceptionally detailed view of this extreme phenomenon, yielding numerous results in both astrophysics and physics, particularly on the behavior of ultra-dense matter. However, despite enormous efforts, no new multi-messenger detections have been made since. This is due to the formidable observational challenge posed by the rapid and precise alerts of GWs, the immediate reactivity of a network of telescopes, and the online data processing required for the identification of EM counterparts.The identification of EM counterparts enables numerous high-priority scientific studies, such as constraints on the equation of state of neutron stars, the measurement of the universe's expansion rate, and the r-process nucleosynthesis of heavy elements produced during a kilonova. For a rapid follow-up of possible counterparts to these events, we must reduce the sky-localization area where the event occurs. However, the significantly different sensitivities of the detectors demonstrate how challenging gravitational-wave (GW) follow-up can be. This is the case for the fourth (ongoing) and fifth LIGO/Virgo/KAGRA (LVK) observation campaigns. Many GW signals from compact binary mergers are hidden by detector noise and can be detected if the noise is sufficiently reduced. To maximize the scientific outcome of the LVK GW detectors, such as the detectability of pre-merger signals, noise must be significantly reduced. Several factors contribute to this noise, undermining the detector's sensitivity, including environmental noise, instrumental artifacts, and some more fundamental and irreducible noises. The identification of additional sub-threshold events is therefore linked to our ability to reduce noise in the instruments. Noise and sensitivity directly influence our capacity to extract information from GW signals.To mitigate these effects, I initially developed new tools and techniques while also making several improvements to existing ones. These analysis tools include, among others, i) enhancing the capabilities of the Nuclear Multi-messenger Astronomy (NMMA), a Python library for probing nuclear physics and cosmology with multi-messenger analysis; ii) updating and configuring telescopes such as the Zwicky Transient Facility (ZTF), the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), and the Ultraviolet Transient Astronomy Satellite (ULTRASAT) within Gravitational-wave Electromagnetic Optimization (gwemopt), a tool for simulating detections using a telescope and event sky map information; iii) injecting a new distribution, PBD/GWTC-3, into Ligo.Skymap for "observing scenarios". This new distribution can define all populations of compact binary coalescences with a single law; iv) developing NMMA-Skyportal, a pipeline that integrates ZTF alerts, the Skyportal tool, a collaborative platform for time-domain astronomy, and NMMA to discriminate the nature of light curves in real-time.Moreover, this work provides projections for astronomers interested in data produced by GW detectors, as well as expected constraints on the universe's expansion rate based on forthcoming data. These results are useful to those analyzing GW data and those seeking EM counterparts to neutron star mergers. Finally, to address the problem of "astrophysical signals bathing" below the noise threshold, I applied the DeepClean algorithm, a one-dimensional convolutional neural network, to estimate, analyze and subtract stationary and non-stationary noises in the Virgo detector. A first for the Virgo detector. In addition to preserving the integrity of the astrophysical signal, the algorithm improves the detector's signal-to-noise ratio.