Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by its heterogeneity, posing challenges in predicting disease progression and tailoring interventions. This study leverages machine learning algorithms to analyze multi-modal datasets, including clinical records, imaging, and genetic information, with the aim of developing a prognostic prediction model for ALS. Through the integration of advanced ML techniques, we seek to unveil hidden patterns within the data that can inform more accurate prognosis. The model's outputs not only contribute to our understanding of disease trajectories but also hold potential for personalized treatment strategies. This research represents a pivotal step towards harnessing the power of ML in refining prognostic predictions for ALS, ultimately improving patient care and outcomes.