This thesis investigates multi-scale modelling approaches for Powder Bed Fusion using a Laser Beam (PBF-LB) of nickel–titanium (NiTi), a shape memory alloy whose functional performance is highly sensitive to thermal history and composition. The objective was to improve melt pool predictability, process optimisation, and residual stress control through an integrated experimental and modelling framework. Single-track experiments were performed to investigate melt pool behaviour under varying laser power and scanning speed. Three analytical melt pool models were evaluated, with the Eagar–Tsai model predicting melt pool width with a mean absolute error below 10% and the Gladush–Smurov model predicting melt pool depth with errors below 20%. These findings highlight the limitations of single-model approaches and motivate hybrid predictive strategies for NiTi melt pool characterisation. Building on these findings, a high-fidelity thermal finite element model (FEM) incorporating CALPHAD-derived temperature-dependent material properties was developed and validated against experimental measurements, achieving prediction errors below 10% for melt pool dimensions. A multi-output machine learning surrogate model trained on the simulation data achieved coefficients of determination (R2) greater than 0.95, enabling rapid melt pool prediction across a broad process window. A comprehensive multi-modal residual stress experimental characterisation was performed using hole-drilling, X-ray diffraction (XRD), and in-situ pyrometric measurements. A previously unreported non-monotonic relationship between volumetric energy density (VED) and residual stress was identified, revealing stress saturation and relaxation regimes governed by process parameters. A part scale thermo-mechanical finite element model was subsequently developed to predict residual stress from thermal history, phase evolution, and cantilever beam deflection, predicting von Mises residual stresses in the range of 35–120 MPa. Collectively, this work established an integrated framework linking process parameters, melt pool behaviour, thermal history, phase evolution, and residual stress development, providing practical tools for improving the reliability of additively manufactured NiTi components.