Mistuning affects forced response of bladed disks drastically; therefore, its identification plays an essential role in the forced response analysis of bladed disk assemblies. Forced response analysis of mistuned bladed disk assemblies has drawn wide attention of researchers but there are a very limited number of studies dealing with identification of mistuning, especially if the component under consideration is an integrally bladed disk (blisk). This paper presents two new methods to identify mistuning of a bladed disk from the assembly modes via utilizing cascaded optimization and neural networks. It is assumed that a tuned mathematical model of the blisk under consideration is readily available, which is always the case for today's realistic bladed disk assemblies. In the first method, a data set of selected mode shapes and natural frequencies is created by a number of simulations performed by mistuning the tuned mathematical model randomly. A neural network created by considering the number of modes, is then trained with this data set. Upon training the network, it is used to identify mistuning of the rotor from measured data. The second method further improves the first one by using it as a starting point of an optimization routine and carries out an optimization to identify mistuning. To carry out identification analysis by means of the proposed methods, there are no limitations on the number of modes or natural frequencies to be used. Thus, unlike existing mistuning identification methods they are suitable for incomplete data as well. Moreover, since system modes are used rather than blade alone counterparts, the techniques are ready to be used for analysis of blisks. Case studies are performed to demonstrate the capabilities of the new methods by using two different mathematical models to create training data sets a lumped-parameter model and a relatively realistic reduced order model. Throughout the case studies, the effects of using incomplete mode families and random errors in assembly modes are investigated. The results show that, the proposed method utilizing cascaded optimization and neural networks can identify mistuning parameters of a realistic blisk system with an exceptional accuracy even in the presence of incomplete and noisy test data.