This thesis investigates the application of advanced machine learning (ML) techniques for reactor physics applications, specifically in the area of lattice and nodal calculations. The research is divided into two main areas. The first focuses on accelerating the generation of few-group cross section (FGXS) data and the second area focuses on the use of ML for accurate predictions of nodal parameters and their uncertainty estimates.   In the first area, ML-based surrogate models were developed to predict multi-group cross section (MGXS) libraries for nuclides commonly found in pressurized water reactors (PWRs), serving as efficient alternatives to conventional tools such as XSPROC. Additionally, a hybrid model incorporating two deep neural networks (DNNs) with a linear blending scheme was proposed to simulate the depletion-driven evolution of nuclide compositions within fuel pellets. To manage the high dimensionality of MGXS data, Principal Component Analysis (PCA) was employed to construct a reduced latent space, which was then mapped using DNNs conditioned on reactor state parameters. Recurrent neural networks (RNNs) were also evaluated for modeling fuel depletion behavior. Two RNN variants—the “Direct NN” and the “Difference NN”—were developed and compared, and a nuclide-specific blending parameter optimized during training was introduced to enhance predictive performance without requiring additional training data.  The second area examines ML-based approaches for nodal data representation. Statistically rigorous model comparisons were performed using the non-parametric Wilcoxon, Nemenyi, McDonald-Thompson (WNMT) test. The study demonstrated that ML models can predict not only mean FGXS parameters but also their associated covariance matrices, capturing aleatoric uncertainty and enabling their integration into full-core uncertainty quantification frameworks. Both polynomial regression and DNN-based models were assessed, alongside the effects of descriptive statistical preprocessing. Results showed a strong dependence of model accuracy on training dataset size, with polynomial regression combined with descriptive statistics yielding the most accurate predictions on the largest dataset.