The global demand for energy has increased exponentially over the years. To reduce the dominance of fossil fuels in energy production, there has been a shift towards energy production models based on renewable sources. In the design of hybrid energy systems, it is essential to keep investment costs low while ensuring the security of the energy supply by meeting the consumer's energy demands without interruption. The success of a good energy production model can be directly associated with the results of load estimation. The primary objective of this research is to predict the electricity demand for the Göksun district until 2028, utilising a data set that encompasses electricity usage from 2019 through the first four months of 2024 for the Göksun district in Kahramanmaraş. This endeavour includes the application of various machine learning (ML) paradigms (long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN)- LSTM, support vector regression (SVR)) to produce load forecasting outcomes and to engineer an optimally performing hybrid system. On evaluation of the performance metrics derived from the experimental data, it has been established that the LSTM model outperforms other methodologies, yielding more favourable results. The simulation studies of the designed hybrid system were conducted using the hybrid optimisation model for electric renewables software (HOMER Pro), demonstrating improvements in both economic and environmental parameters. Our study is unique in that it is the first to utilise a data set specific to the Göksun region and to model predictions obtained from this data set using HOMER software. [ABSTRACT FROM AUTHOR]