This study demonstrates various artificial intelligence methods to predict and optimise blast-induced rock fragmentation at Orapa Diamond Mine in Botswana. These techniques include an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS), a genetic algorithm with ANN (GA-ANN), and particle swarm optimization with ANN (PSO-ANN). A collection of data from 120 blasting events with nine input parameters at the mine was utilized for this task. The results indicate that the PSO-ANN model outperforms other models in predicting blast-induced fragmentation. We used the optimal PSO-ANN model to optimise fragmentation, identified using the Monte Carlo method. The optimal model consists of nine inputs, two hidden layers with 65 and 30 neurons, and one output (7-65-30-1). Using gradient descent, we navigated this ten-dimensional solution space to determine the optimised blast design parameters and achieved an optimal fragmentation value of approximately 86 %. Sensitivity analysis results reveal that the most influential input parameters on fragmentation are rock factor (15.3 %), blastability index (14.7 %), and spacing-to-burden ratio (14.7 %). In contrast, the stiffness ratio has the least influence on fragmentation (6.3 %).