Laser processing techniques are widely used in industrial applications for their repeatability and reliability. However, the optimization of a laser process for a specific application is challenging and require detailed experimental investigations to determine the input processing conditions and parameter values that deliver high repeatability and reliability. The objective of this doctoral work was therefore to develop prediction models for laser-based processing techniques to understand the laser processing parameter relationship with the output properties and to forecast events not observed experimentally. The important techniques of Selective Laser Sintering (SLS), Laser Surface Texturing (LST),and Selective Laser Melting (SLM) were selected for development of the predictive models. For SLS of glass filled polyamide parts, an Adaptive Neuro-Fuzzy Inference system using Simulated Annealing method (ANFIS-SA) and Grey Relational Analysis (GRA) were utilised to determine processing parameters (laser power and scan speed, spacing and length) delivering best mechanical properties (tensile strength and elongation). ANFIS-SA system outperformed the GRA in finding optimal solutions for the SLS process applied for glass fiber reinforced part production. For LST study, Artificial Intelligence (AI) models were developed to predict the properties (diameter increase, insertion force and pullout force) of laser processed stainless steel 316 samples used for interference fit. Artificial Neural Network (ANN) and ANFIS were used to predict the characteristics of laser surface texturing. The models based on feedforward neural network (FFNN) were used to examine the effect of the laser process parameters for surface texturing on 316L cylindrical pins. This study demonstrated that ANFIS prediction was 48% more accurate compared to that provided by the FFNN model. Stainless steel 316L cylindrical pins with defined surface structures for interference fit application were manufactured by the Selective Laser Melting Additive Manufacturing technique. The fabricated pins were assessed for resulting bond strength within interference fit joints. The effects of texture profile on the insertion and removal forces were investigated using Box-Behnken design of Response Surface Methodology (RSM) and results are presented and discussed. ANalysis Of VAriance (ANOVA) was used to check the adequacy of the developed empirical relationships. Two quadratic models were generated. One for correlation between profile geometry and insertion force and second for relating the profile geometry to removal force. The models were validated using experimental results and demonstrated good agreement with less than 10% error.