Despite the advances in neural text to speech (TTS), many Arabic dialectal varieties remain marginally addressed, with most resources con- centrated on Modern Spoken Arabic (MSA) and Gulf dialects, leaving Egyptian Arabic— the most widely understood Arabic dialect— severely under-resourced. We address this gap by introducing NileTTS: 38 hours of tran- scribed speech from two speakers across di- verse domains including medical, sales, and general conversations. We construct this dataset using a novel synthetic pipeline: large language models (LLM) generate Egyptian Arabic content, which is then converted to natu- ral speech using audio synthesis tools, followed by automatic transcription and speaker diariza- tion with manual quality verification. We fine- tune XTTS v2, a state-of-the-art multilingual TTS model, on our dataset and evaluate against the baseline model trained on other Arabic dialects. Our contributions include: (1) the first publicly available Egyptian Arabic TTS dataset, (2) a reproducible synthetic data gen- eration pipeline for dialectal TTS, and (3) an open-source fine-tuned model. All resources are released to advance Egyptian Arabic speech synthesis research.