Using the latest technology for natural language processing is important for defining the appropriate terminology and temporal and numeric expressions in an open-domain text. The machine and deep learning techniques can help to improve the performance of various Natural Language Processing (NLP) applications, such as Information Extraction (IE), Information Retrieval (IR), and question-answering (QA) tasks. Artificial intelligence systems aim to identify and classify Arabic terminology within Arabic text. Millions of data feeds are published in news articles, blogs, letters, and more about climate and weather events. Whereas, the ability to automatically organize and handle them is becoming indispensable and hard because of the traditional methods in the natural language processing field, due to the lack of sufficient research based on the Arabic text. In this work, the researchers designed and implemented an element of a climate and weather events extraction system from Arabic news text, using supervised machine learning through the ANN classification algorithm. ANN is an effective classification algorithm, it is based on different text processing steps such as data acquisition, preprocessing (Tokenization, removing noise), and Segmentation (Labeling), and is to be prepared to extract climatic and weather events by BOW, from the news text in the Arabic language, and then classification it. The results show that applying the word embedding feature extraction technique as well as the Chi-square feature selection technique based on the MLP neural network supervised machine learning classifier has given a high accuracy performance of the system an F-measure of 98%.