As India advances toward achieving a better quality of life, a significant segment of the population, particularly especially abled individuals, and patients with Amyotrophic Lateral Sclerosis (ALS), still faces substantial challenges in daily living. ALS is a progressive neurodegenerative disease that leads to muscle control loss and eventually complete paralysis, though cognitive functions generally remain intact. This research focuses on developing a cost-efficient, controlled Graphical User Interface (GUI) that allows ALS patients and others with severe motor impairments to navigate essential on-screen functions with minimal effort through multi-directional Electrooculography (EOG) eye movements. An experiment was conducted with 10 participants to collect raw sensor values for multi-directional eye movements. Each participant's eye movements were carefully labeled, and the collected data was analyzed to discern behavioral patterns using graphical representations. This analysis aimed to understand the efficacy of the eye movement detection system, providing insights into refining algorithms for the controlled GUI interface. A Support Vector Machine (SVM) model was implemented for data classification, achieving an accuracy of 83.33%. Additionally, a threshold-based classification approach was utilized for real-time prediction of data, distinguishing between upward and downward eye movements with an accuracy of 92.5% while synchronizing data streams within 100 ms at a frequency of 75 Hz. Furthermore, a GUI was developed specifically to be controlled with eye movements, offering an accessible interface for ALS patients.