Solar radio bursts (SRB) play a crucial role in understanding solar activity and its influence on systems across the world. The classification of SRB into distinct types based on morphology and frequency drift requires vast data and poses significant challenges for automated detection and classification. In this paper, we introduce a deep learning-based approach to address this challenge by leveraging a curated dataset from the CALLISTO network and a ground-based radio astronomy station. A convolutional neural network is trained to identify and classify SRB, despite the low signal-to-noise ratios, dynamic solar atmosphere and limited training data. This work contributes to the automation of SRB analysis and showcases the potential of deep learning in decoding complex astrophysical phenomena. Preliminary results demonstrate the efficacy of our approach, paving the way for a revolutionary advancement in the field of SRB analysis.