In this study, we developed a fixed-dose combination (FDC) tablet of pitavastatin and fenofibrate used to treat dyslipidemia using our own artificial intelligence (AI)-based formulation design program. The AI program was utilized to investigate the physicochemical properties of pitavastatin and determine the optimal ratio of disintegrants and binders that would shown a dissolution profile similar to the reference drug. The AI program predicted 12 physicochemical properties of pitavastatin (solubility, log P, pKa, M.W., etc.) based on the SMILES (simplified molecular input line entry system) of the API, with a comparison accuracy of 82.3%. The design space (DS) of the disintegrant and binder ratio was established for optimal formulation design, and the dissolution profile was confirmed to be equivalent to that of the reference drug through experiments. The predicted DS and the DS obtained through the experimental design method showed a 85% agreement. This study demonstrated the potential of AI programs to predict the physicochemical properties of drugs and identify the ratio of disintegrants and binders that result in a dissolution profile similar to the reference drug, which is crucial in the formulation design of FDC tablets. Furthermore, it was verified that the use of deep learning-based AI models can be an effective tool to accelerate and improve the formulation development process by 75%.