In this dissertation, I explored relational learning via latent variable models. Traditional machine learning algorithms cannot handle many learning problems where there is a need for modeling both relations and noise. Statistical relational learning approaches emerged to handle these applications by incorporating both relations and uncertainties in these problems. Latent variable models are one of the successful approaches for statistical relational learning. These models assume a latent variable for each entity and then the probability distribution over relationships between entities is modeled via a function over latent variables. One important example of relational learning via latent variables is text data modeling. In text data modeling, we are interested in modeling the relationship between words and documents. Latent variable models learn this data by assuming a latent variable for each word and document. The co-occurrence value is defined as a function of these random variables. For modeling co-occurrence data in general (and text data in particular), we proposed latent logistic allocation (LLA). LLA outperforms the-state-of-the-art model—latent Dirichlet allocation—in text data modeling, document categorization and information retrieval. We also proposed query-based visualization which embeds documents relevant to a query in a 2-dimensional space. Additionally, I used latent variable models for other single-relational problems such as collaborative filtering and educational data mining. To move towards multi-relational learning via latent variable models, we propose latent feature networks (LFN). Multi-relational learning approaches model multiple relationships simultaneously. LFN assumes a component for each relationship. Each component is a latent variable model where a latent variable is defined for each entity and the relationship is a function of latent variables. However, if an entity participates in more than one relationship, then it will have a separate random variable for each relationship. We used LFN for modeling two different problems: microarray classification and social network analysis with a side network. In the first application, LFN outperforms support vector machines—the best propositional model for that application. In the second application, using the side information via LFN can drastically improve the link prediction task in a social network.