This chapter outlines a methodological framework for utilizing advanced machine learning (ML) methods to optimize lipid nanoparticle formulations through in silico screening. It highlights the integration of chemoinformatic methodologies, such as molecular fingerprinting and molecular descriptor extraction, to generate comprehensive molecular profiles of LNP components. The chapter systematically describes supervised and semi-supervised ML approaches, emphasizing the utilization of virtual screening strategies including both ligand- and structure-based. A critical exploration of pseudo-labeling techniques is included to demonstrate leveraging unlabeled datasets to expand training data and improve predictive accuracy. The chapter underscores how these computational methodologies dramatically accelerate LNP candidate identification compared to traditional experimental screening, ultimately reducing the time and resource demands in nanomedicine development.