Among mental health diseases, depression is one of the most severe, as it often leads to suicide which is the fourth leading cause of death in the Middle East. In the Middle East, Egypt has the highest percentage of suicidal deaths; due to this, it is important to identify depression and suicidal ideation. In Arabic culture, there is a lack of awareness regarding the importance of diagnosing and living with mental health diseases. However, as noted for the last couple years people all over the world, including Arab citizens, tend to express their feelings openly on social media. Twitter is the most popular platform designed to enable the expression of emotions through short texts, pictures, or videos. This paper aims to predict depression and depression with suicidal ideation. Due to the tendency of people to treat social media as their personal diaries and share their deepest thoughts on social media platforms. Social data contain valuable information that can be used to identify user’s psychological states. We create AraDepSu dataset by scrapping tweets from twitter and manually labelling them. We expand the diversity of user tweets, by adding a neutral label (“neutral”) so the dataset include three classes (“depressed”, “suicidal”, “neutral”). Then we train our AraDepSu dataset on 30+ different transformer models. We find that the best-performing model is MARBERT with accuracy, precision, recall and F1-Score values of 91.20%, 88.74%, 88.50% and 88.75%.