This thesis investigates the Intelligent Computational Nanotechnology, that is, the support of Computational Intelligence (CI) techniques in the challenges faced by the Nanoscience and Nanotechnology. For example, Neural Networks are used for build Inference systems able to relate a set of input parameters with the final characteristics of the nanostructures, allowing the researchers foresees the behavior of other nanostructures not yet realized experimentally. From the inference systems, Genetic Algorithms are then employees with the intention of find the best set of input parameters for the synthesis (project) of a desired nanostructure. In another line of inquiry, the Genetic Algorithms are used for the base functions optimization used in ab initio calculations. In that case, the exponents of the Gaussian functions that compose the base functions are optimized. In another approach, the Genetic Algorithms are applied in the optimization of molecular and atomic clusters, allowing the researchers to theoretically study the experimentally formed clusters. Finally, the use of these algorithms, use together with simulators, is applied in the automatic synthesis of OLEDs and circuits of Quantum Dots Cellular Automata (QCA). This research revealed the potential of the CI in innovative applications. The hybrid systems of optimization and inference, for example, conceived to foresee the height, the density and the height deviation of self-assembled quantum dots, present high levels of correlation with the experimental results and low percentage errors (lower to 10%). The Young’s module of nanocomposites is also predicted by a similar system and presents percentage errors even smaller, around 4%. The Genetic Algorithms, jointly with the package of molecular modeling Gaussian03, optimize the parameters of functions that generate exponents of primitive Gaussian functions of base sets for hartree-fock calculations, obtaining smaller energies than those presented in the literature. In another application, the Genetic Algorithms are also efficient in the search by the low energy geometries of the atomic clusters of (LiF) nLi +, (LiF) n and (LiF) nF-, obtaining a set of new isomers yet not propose in the literature. A similar methodology is applied in an unpublished system for understand the formation of molecular cluster of ionic H2O from neutral clusters. The results show how the clusters can be obtained from different perspectives, forming structures not yet investigate in the scientific area. This work also presents the automatic synthesis of robust QCA circuits. The circuits obtained present high polarization, similar to those proposed by the specialists, but with an important reduction in the quantity of cells. Finally, a system involving Genetic Algorithms and an analytic model of multilayer OLEDs optimize the concentrations of organic material in each layer in order to obtain more efficient devices. The results reveal a device 9.7% better that the solution found in the literature, being these results verified experimentally. In summary, the results of the proposed research allow observe that the unpublished integration of the techniques of Computational Intelligence with Computational Nanotechnology, here named Intelligent Computational Nanotechnology, emerges as a promising alternative for accelerate the researches in Nanoscince and the development of application in Nanotechnology.