Over the decades, hybrid halide perovskite (HHP)-based solar cells have emerged as the groundbreaking technology in the domain of renewable energy sector owing to their low fabrication cost and impressive power conversion efficiency (PCE) of 27.3% at present. Despite of several remarkable properties of HHP material like tunable band gap, low exciton binding energy, high charge carrier mobility, high diffusion length, facile and low-cost-based fabrication process, these perovskite solar cells (PSCs) are still far from the door of photovoltaic market. Therefore, there is a need to explore and analyze the PSCs from the computational perspective along with the experimental approach. Computational analysis has the potential to significantly advance the development of perovskite-based photovoltaics, allowing the prediction of material properties, optimization of device designs, and the investigation of new perovskite compositions. This chapter deals with the importance of computational analysis in PSC research along with the comprehensive discussion on various computational approaches including Density Functional Theory (DFT), molecular dynamics (MD) simulations, and machine learning (ML). Various device simulation and modeling software such as SCAPS-1D, COMSOL, and AMPS are further discussed in this chapter. In addition, future directions for further research and development in the field of PSCs have also presented. The computational approach is expected to play a significant role in shaping the future of sustainable energy technologies and realizing the commercial potential of PSCs.