Epilepsy is a brain disorder characterized by recurrent episodes of abnormal brain activity, known as epileptic seizures, and this condition can have a significant impact on patients’ quality of life, as well as their emotional, social, and physical well-being. This study investigates the development and evaluation of a signal processing-based algorithm for detecting tonic-clonic epileptic seizures using a smartphone accelerometer. The algorithm was implemented using Python and evaluated using a dataset available on IEEE DataPort. Data were analyzed in the frequency domain, calculating energies for different types of normal and epileptic activities. The results indicate that the developed algorithm is capable of distinguishing between normal and epileptic states, with a precision of 42.8%, an accuracy of 88.2%, and a specificity of 87.1%. This approach provides an effective tool for detecting epileptic episodes, thereby improving clinical management and patients’ quality of life.