A neurological disease called Amyotrophic Lateral Sclerosis exists that damages brain and spinal cord nerve cells, causing muscular atrophy and eventually paralysis. Use a Support vector machine (SVM) for Amyotrophic Lateral Sclerosis diagnosis as part of machine learning to get the most accurate results for categorizing health data. Early Amyotrophic Lateral Sclerosis diagnosis is essential for effective treatment. A supervised machine learning approach that excels in large-scale settings is the support vector machine. It is especially helpful in tackling complex problems with high-dimensional data where the characteristics and the goal variable have a non-linear relationship. In Some previous the functions are poorly connected and the use of classification models is appropriate for portable datasets that obscure key patient information are best suited for short dimensional spaces, according to previous research applying the Functional Rating Scale score for Amyotrophic Lateral Sclerosis yields the least trustworthy conclusions. The ALSFRS-R score function reveals discrepancies in the bulbar, limb, and respiratory functions. The accuracy of the diagnosis can be impacted by a number of factors that alter surface electromyography (sEMG) signals, including electrode location, muscle exhaustion, and movement artefacts. Our findings imply that ML-based techniques may enhance the speed and diagnostic precision of ALS identification, which may result in better outcomes for people with this crippling condition.