A multi-scale modeling approach is employed for the study of the effect of oleic-acid (OA) coverage on the magnetic behaviour of Co ferrite nanoparticles (CFNs), using high performance computing (HPC). Our study is performed in three different length scales: first we perform electronic structure calculations using parallel implementation of the density functional theory (DFT) method to study the magnetic properties of ultra-small OA coated CFNs. Next taking input from the DFT data, we calculate the magnetic characteristics of larger in size OA coated CFNs at an atomic scale performing Monte Carlo simulations. Finally, a mesoscopic modeling approach for interacting assemblies of nanoparticles is employed to reduce further computation time and sources for the study of the magnetic behaviour of CFNs covered with different percentage of oleic-acid, at finite temperature. The results demonstrate that the DFT magnetic moment and magnetic anisotropy of the nanoparticle decrease with the increase of the percentage of the surfactant. However, in the assembly of CFNs the interplay between the exchange and dipolar inter-particle interactions results in the increase of the magnetic anisotropy and the decrease of the saturation magnetization as the percentage of OA coverage increases, in agreement with experimental findings. The proposed multi-scale computational approach, implemented in HPC environment, illustrates its ability to handle numerical calculations on complex magnetic interactions of multiple structural components. It can overcome computational limitations to predict optimum parameters for hybrid organic/inorganic nanomaterials for various applications.