The development of effective and safe drugs is a complex and resource-intensive process that often relies on uncertain trial-and-error methods. Predicting pharmacokinetic properties, such as drug delivery, is decisive for accelerating drug discovery and enhancing therapeutic outcomes. This paper presents a Machine Learning (ML) and Deep Learning (DL) based approach utilizing open pharmacological databases to predict properties associated with drug distribution, with a focus on bioavailability and the octanol-water partition coefficient (LogP). The study encompasses data preprocessing, molecular representation via SMILES encoding, and model evaluation utilizing regression and classification metrics. Results show promising predictive performance, suggesting that ML and DL techniques can optimize early drug discovery stages and support decision-making.