Peptides are molecular entities with a diverse set of functionalities vital for biological processes and biotechnological applications. Among their roles, the ability of peptides to bind to solid materials has gathered attention, particularly as building blocks in constructing bio-nano interfaces and molecular linkers. Directed evolution techniques such as iterative phage display, have emerged as capable tools for identifying peptides and proteins with specific affinities for various targets despite its constraints, particularly its low-throughput nature. Those limits have motivated the work on more advanced methodologies such as deep-directed evolution, which integrates high-throughput sequencing. By collecting massive amounts of data, deep-directed evolution provides a broad landscape of sequence information, thus enabling computational modeling and optimization of peptide sequences. This thesis aims to develop machine learning workflows that capture the sequence-function relationship from the data, allowing the design of peptides with desired functionalities. Two machine learning approaches were employed: the Random Forest algorithm (RF) and deep neural networks (DNN). By aggregating binding score predictions from the two models, the predictor achieved a Pearson correlation coefficient of 0.904 and a mean absolute error of 0.0304 on the high- confidence test set and was employed to design a candidate peptide as a proof of principle. Our findings emphasize the importance of including domain knowledge via peptide abundance weighting and amino acid encoding types while designing training strategies. The procedures outlined in this work demonstrate key steps towards designing a peptide sequence-function prediction platform with broad implications for bio-nanotechnology and engineering.