This research aims to develop an Arabic text-to-speech (TTS) service with Syrian dialect, which is a variety of Arabic spoken in Syria and some neighboring countries, with easy access to it for people with disabilities or difficulty reading Arabic, such as people with visual impairments or learning disabilities. To achieve this goal, we employ two state-of-the-art Machine Learning (ML) approaches: Tactron2 and Transformers, which have achieved impressive results in various natural language processing tasks, including TTS. We compared the two approaches and evaluated the resulting TTS service using subjective measures. Our results show that both approaches can produce high-quality speech in the Syrian dialect, but transformers have the advantage of being more efficient and more flexible in handling different languages and accents.