The fifth Nuanced Arabic Dialect Identification (NADI 2024) shared task aimed to advance Arabic NLP by providing standardized evaluation conditions, datasets, and modeling opportunities for dialect identification, dialectness estimation, and dialect-to-MSA machine translation. A total of 51 teams registered, with 12 participating and submitting 76 valid submissions. Three teams participated in Subtask 1 (dialect identification), three in Subtask 2 (dialectness estimation), and eight in Subtask 3 (dialect-to-MSA translation). The winning team achieved 50.57 F1 score in Subtask 1, 0.1403 RMSE in Subtask 2, and 20.44 BLEU in Subtask 3. Results indicate that Arabic dialect processing remains challenging. Subtask 1 involved multi-label dialect identification, Subtask 2 estimated dialectness on a scale of 0-1, and Subtask 3 focused on dialect-to-MSA translation. The task used geolocated tweets and annotated datasets to evaluate models. The evaluation metrics included F1, RMSE, and BLEU scores. The task highlighted the need for further research in dialect identification and translation, with a focus on improving models for dialectal Arabic. The shared task provided a platform for researchers to collaborate and advance Arabic NLP.The fifth Nuanced Arabic Dialect Identification (NADI 2024) shared task aimed to advance Arabic NLP by providing standardized evaluation conditions, datasets, and modeling opportunities for dialect identification, dialectness estimation, and dialect-to-MSA machine translation. A total of 51 teams registered, with 12 participating and submitting 76 valid submissions. Three teams participated in Subtask 1 (dialect identification), three in Subtask 2 (dialectness estimation), and eight in Subtask 3 (dialect-to-MSA translation). The winning team achieved 50.57 F1 score in Subtask 1, 0.1403 RMSE in Subtask 2, and 20.44 BLEU in Subtask 3. Results indicate that Arabic dialect processing remains challenging. Subtask 1 involved multi-label dialect identification, Subtask 2 estimated dialectness on a scale of 0-1, and Subtask 3 focused on dialect-to-MSA translation. The task used geolocated tweets and annotated datasets to evaluate models. The evaluation metrics included F1, RMSE, and BLEU scores. The task highlighted the need for further research in dialect identification and translation, with a focus on improving models for dialectal Arabic. The shared task provided a platform for researchers to collaborate and advance Arabic NLP.