The use of deep learning for food recognition has attracted significant attention due to its applications in dietary monitoring, automated food logging, and nutrition analysis. While deep learning has demonstrated impressive results across various cuisines, some, including Saudi Arabian cuisine, have not been thoroughly explored. This paper examines the effectiveness of deep learning models in accurately classifying and recognizing thirteen different Saudi Arabian traditional food dishes. We applied convolutional neural networks (CNNs) and transformer-based architectures, taking advantage of large pre-trained models for fine-tuning on our dataset. Additionally, we have curated the first dataset of traditional Saudi Arabian food, consisting of 13 categories and 3,000 images. Experimental results show that deep learning models achieve high accuracy in distinguishing among diverse food categories, even under challenging conditions such as occlusions and varying lighting. Our curated dataset is available publicly at https://huggingface.co/datasets/ssalahmari/Saudi_Traditional_Food.