This study examines the settlement behaviour of neighbouring foundation footings embedded in a spatially rotated anisotropic random field. In this context, soil properties at any location are correlated and random due to the interconnection of soil heterogeneity at various orientation angles. In the reliability-based approach of the foundation settlement analysis, the spatial variability of random fields, including their location and distribution, plays a significant role in the probabilistic model simulation of uncertainty propagation (Lin et al., 2024). The study considers the directional dependence of spatial correlation length, altering the major and minor principal directions of spatial correlation length at different orientation angles to predict the probability of settlement. The Hardening Soil Model (HSM) is used to simulate soil behaviour, with the unloading-reloading stiffness modulus (E_ur) and the internal friction angle (φ) modelled as spatially varying random variables considering their dependent variables. Fourier series and the Cholesky decomposition method are employed for random generation, while Monte Carlo simulations are used to examine the statistical distribution of settlement responses and evaluate the convergence stability of these responses. The effects of varying horizontal spatial correlation lengths (SOFx = 1m to 1000m) and vertical spatial correlation lengths (SOFy = 0.5m and 1.0m) are also explored. The study also investigates the influence of spatial variability and directional dependence of soil properties on settlement responses, considering distinct spatial patterns of rotated major and minor spatial correlation directions at different orientation angles (0° to 90°). To this end, the study also identifies the spatial scale fluctuations and random field rotation angles that influence the mean, coefficient of variation (COV), and probability distribution of settlement. Keywords: Neighbouring footing, settlement, spatial correlation length, spatial Random field, Rotated anisotropy, reliability analysis