Tasks in architectural and interior design range from defining the building floor plans and ensuring desired functionality, to deciding furnishing styles and arrangement choices; all to best fit certain pre-established purposes. The process of design, as a whole, has remained hard to master for computer-based optimization in general and for computational intelligence approaches in particular. Some attempts to tackle different subfields of this problem in a machine learning fashion have emerged over the last few years, aiming to offer partial automatization of human tasks, personalized support for specialists in the field and professional guidance for amateurs. In this thesis, we first present an overview of current advances of computational intelligence in architectural science with a focus on interior design. We describe various learning models applied to interior design challenges such as furniture type selection, style compatibility, furniture arrangement, or ornamental decoration. The core of the thesis is devoted to report ongoing research towards the development of a commercial, robust and scalable solution for automatic furniture arrangement, given a room plan. We propose two probabilistic models to be used in the complex problem of furnishing bedrooms. The first resides in a Bayesian Network based approach for the automatic generation of the number and types of furniture entities to occupy the new space, namely the occurrence model. The second one, called arrangement model, deals with learning different commonly met sets of items interconnected within the same space and estimating their relative positions with GMMs. Both models heavily contribute to the main goal of achieving a 3D planner for bedrooms, but their genericity allows other types of interiors to be modeled through the same process.