The creative review process often involves manually sifting through numerous images to identify content that violates advertising policies. The manual creative review team spends a lot of time and resources in detecting harmful contents such as gambling within image creatives. Our objective is to reduce the workload of the team with advanced ML models by classifying the images as gambling or not. For this purpose, we have evaluated CNN based model (VGG 16), Vision Transformer model (ViT) and LLMs (Llama Vision 11b and LLM2Vec encoder), along with an ensemble of these for the gambling image classification task. We are able to achieve close to 99% accuracy with misclassification for actual gambling and actual non-gambling being 5% and 1% respectively. We have also achieved an approximately 95% potential reduction in time, effort and cost on application of our classification solution.