This dissertation focuses on the transport of nanoparticles through nanopores - nanoparticle translocation - and how this phenomenon can be used as a characterization tool known as nanopore sensing. Nanoparticles not only occur widely in nature but also have been extensively engineered in academic research and industrial development. Due to their unique properties, nanoparticles are used in several industrial applications. Characterization tools that are accessible, easy to use, and robust are key for both research and quality control in nanoparticle science and technology. Rarely all these desirable characteristics are encountered in a single characterization tool. For example, Dynamic Light Scattering (DLS) is known to provide easy measurements of nanoparticle size but is prone to errors when analyzing dispersed and mixed distributions. On the other hand, direct visualization of the particles by Transmission Electron Microscopy (TEM) provides accurate information on particle size but is difficult to perform with high throughput and is prone to sampling bias. Nanopore sensing can, however, measure physical properties both at a single particle level and with high throughput. Experiments were successful in characterizing particle concentration, size, and charge. However, the experimental results are not always readily interpretable. In response, modeling and simulation tools are used to shed light on the complex relationships coming from the nanoscale environment. Despite the great amount of development in this area, there is still a lack of trajectorydependent simulations that can effectively reproduce the pulses from the translocation of a freely interacting particle. Here, Poisson-Nernst-Planck formalism was combined with Machine Learning and Dynamic Monte-Carlo to form a simulation tool that captures the drift-diffusion motion of hard particles and the current pulses corresponding to these trajectories. Spheres and rods were simulated translocating pores of different dimensions. The simulations suggest inherent limitations in resolution due to the Brownian effect. Whereas previous studies were able to simulate only a few trajectories or estimated the statistics of the features using theoretical arguments, in this study hundreds of trajectories were simulated, calculating statistics directly from the population of results. The framework developed in this research can be expanded to investigate other nanopore systems, helping the development of nanopore sensing.