Electrical load forecasting (ELF) is essential for effective load dispatching and future power system planning, and it has been widely explored, particularly in developed regions. Among its types, short-term load forecasting (STLF) is increasingly important for optimizing electricity usage. This study introduces a novel STLF model designed to address the non-linearity and time-dependent fluctuations in regional load data. The model integrates Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) with a multi-head attention mechanism. While CNN captures spatial features and patterns, the LSTM-GRU combination handles temporal dependencies. The attention mechanism improves the model's focus on critical data segments and enhances interpretability. The architecture is unique, featuring a parallel configuration of CNN and LSTM-GRU sub-models, each equipped with its own multi-head attention mechanism. The model's effectiveness is validated using historical load data from the Chattogram district and other public datasets. Results show that it outperforms several state-of-the-art methods, setting a new benchmark for regional short-term load prediction.