Amyotrophic Lateral Sclerosis (ALS) is a relentlessly progressing neurological disease with limited treatment options. The advent of extensive global datasets and advanced machine learning models offers new opportunities to evaluate potential prognostic, inspection, and diagnostic indicators. Additionally, emerging categorization and staging systems aim to accurately stratify patients into distinct prognostic categories. This study employs an array of machine learning algorithms to predict ALS diagnoses, including decision trees, ensemble methods, gradient boosting algorithms, and support vector machines. Specifically, it uses classifiers like DecisionTree, ExtraTree, Random Forest, Extra-Trees, XGB, LGBM, CatBoost, AdaBoost, SVC, and MLPClassifier. These models range from basic tree-based methods, which split data based on feature values for predictions, to advanced ensemble techniques like Random Forests and gradient boosting, which combine several models to enhance accuracy and robustness. The Support Vector Machine (SVC) identifies the optimal hyperplane to separate classes, while the MLPClassifier, a type of neural network, captures complex data patterns. This diverse approach leverages the unique strengths of each algorithm, providing a comprehensive evaluation of model performance for ALS diagnosis. Results show that the CatBoost classifier achieved the highest performance, with an accuracy of 0.85 and an AUC of 0.97. Other significant models include XGB, RandomForest, and ExtraTrees classifiers, each showing an accuracy of around 0.75 but with varying AUC values.