Remote towns and communities are normally without access to the main electrical grid and electricity is normally generated through diesel generators. Diesel fuel costs represent a significant portion of the utilities' expenditure. Solar photovoltaic (PV) integration is an attractive solution reduces fossil fuel dependency for such communities. This study presents an off-grid hybrid PV/diesel model developed using dynamic modelling and artificial neural network (ANN) techniques. Dynamic subsystem models were developed in Simulink and ANN methods were employed for predictive modelling. Utilizing simple climate data (humidity, rain fall, ambient temperature and wind speed) and load profile as model inputs, generator and PV output powers and fuel consumption can be accurately predicted. Experimental data were used for ANN training and model validation. A comparative analysis was conducted between the Simulink model and an existing industrial design tool for a remote community in the Northern Australia. Simulation results showed that the developed model is a viable planning and analytical tool for aiding future off-grid PV-to-diesel system integration applications, with R2 values ranging from 0.92 to 0.99 and mean relative errors below 5%. Lastly, the incorporation of both dynamic and ANN modelling techniques in a single model reduces modelling complexity whilst maintaining its accuracy and ease-of-use.