This study addresses Amyotrophic Lateral Sclerosis (ALS), a progressive neurodegenerative disease affecting motor neurons. ALS patients experience motor weakness, atrophy, spasticity, and difficulty in speech, swallowing, and breathing. Predicting ALS survival and progression is crucial for tailored care, interventions, and informed decision-making. Utilising the PRO-ACT database and Exonhit Therapeutics' clinical trial data, machine learning may allow predicting disease progression based on ALS Functional Rating Scale (ALSFRS) scores and patient survival within one year of follow-up. To comprehensively understand patient profiles, unsupervised learning via UMAP was applied for dimension reduction, effectively grouping patients based on features. To optimise the predictive quality of our models, we selected features prior to the analysis process. Ridge regression combined with feature selection yielded a predictive quality of 80.32% for patients' survival and UMAP achieved prediction rates from 67.93% when considering all clusters to 92.75% when focusing on the most homogeneous ones, emphasising the relevance of feature selection. Our method showed accurate performance in predicting ALSFRS score progression at 3 months (RMSE = 2.86 and Adjusted R² = 0.796). This study demonstrates the ability of the models to provide meaningful predictive insights in the specific context of ALS, enhancing the understanding of disease dynamics and facilitating personalised patient care.