The presence of biased and prejudicial content in social media has become a pressing concern, given its potential to inflict severe societal damage. Detecting and addressing such bias is imperative, as the rapid dissemination of skewed content has the capacity to disrupt social harmony. Advanced deep learning models are now paving the way for the automatic detection of bias in multimedia content with human-like accuracy. This paper focuses on identifying social bias in social media images. Toward this, we curated a Social Bias Image Dataset (SBID), consisting of 300 bias/no-bias images. The images contain both textual and visual information. We scientifically annotated the dataset for four different categories of bias. Our methodology involves generating a textual representation of the image content leveraging state-of-the-art models of optical character recognition (OCR), image captioning, and character attribute extraction. Initially, we performed fine-tuning on a Bidirectional Encoder Representations from Transformers (BERT) network to classify bias and no-bias, as well as on a Bidirectional AutoRegressive Transformer (BART) network for bias categorization, utilizing an extensive textual corpus. Further, these networks were finetuned on the image dataset built by us SBID. The experimental findings presented herein underscore the effectiveness of these models in identifying various forms of bias in social media images. We will also demonstrate their capacity to discern both explicit and implicit bias.