High-resolution (1)H NMR spectroscopy of biofluids is a good representation of metabolic pattern and offers a high potential noninvasive technique for pathological diagnosis. Diagnosis of thalassemia and quantification of some blood parameters can be performed by using (1)H NMR spectra of human blood serum in parallel with chemometric techniques. Spectra of 28 samples were collected from 15 adult male and female thalassemia patients as experimental set and 13 healthy volunteers as control set. Principal component analysis (PCA) as a dimension reduction tool was used for transforming spectra to abstract factors. The abstract factors were introduced to linear discriminant analysis (LDA), which is a common technique for classification, in order to establish adequate model for discrimination of healthy and unhealthy samples. In addition, these abstract factors were used for calibration of some blood parameters using radial basis function neural network (RBFNN) as an artificial intelligence modeling method. Different test sets (left out samples in training algorithm) were used for evaluating the quality and robustness of the built models. PCA abstract factors were employed as input for LDA model and successfully classified all the members of the test sets except one member of third test set. RBFNN also has a good capability for modeling the most of blood parameters according to proposed network parameters optimization procedure. We conclude that (1)H NMR spectroscopy, LDA and RBFNN assisted by PCA provide a powerful method for thalassemia diagnosis and prediction of some blood variants.