The Greek Parliament is a critical institution for the Greek Democracy, where important decisions are made that affect the lives of millions of people. It consists of representatives from different political parties, and each party has a unique political ideology, stance, and agenda. The proposed research aims to automatically classify parliamentary proceedings to their respective political parties based on the content of their speeches, debates, and discussions. The goal of this research is to assess the feasibility of classifying Greek parliament proceedings to their respective political party via machine learning and neural network algorithms. By using machine learning algorithms and neural networks, the system can learn from large amounts of data and make accurate predictions about the category of a given proceeding. One possible approach is to use supervised learning algorithms, where the system is trained on a dataset of parliamentary proceedings labeled with the respective political parties. The machine learning algorithms can then learn the underlying patterns and features in the text data and accurately classify new proceedings to their respective parties. Another potential approach is to use deep learning neural networks, such as recurrent neural networks (RNNs), to classify the proceedings. These networks can be trained on large amounts of labeled data and can learn the complex relationships between the text features and political parties. The results of this research can be used to gain insights into the political landscape and the positions of different parties on various issues. The ability to automatically classify parliamentary proceedings to their political parties can also aid in political analysis, including tracking the voting patterns of different parties and their representatives and generally the potential revolutionization of social and human sciences is existent. Moreover, the proposed research can have implications for policy-making and governance. By analyzing the proceedings and identifying the political parties' positions and priorities, policymakers can better understand the political landscape and craft policies that align with the values and priorities of different parties. In conclusion, the classification of parliament proceedings, in our case Greek, to their political parties via NLP with machine learning algorithms is a promising research topic that has potential applications in political analysis and decision-making. The ability to automatically classify parliamentary proceedings to their respective parties can enhance transparency and accountability in the democratic system and aid in policy-making and governance.