Most common brain disease is Epilepsy associated with the neurological conditions of a brain activity. The real-time detection of seizure events remains challenges to detect by the traditional methods. To overcome the limitations of traditional method, this study suggests a novel method performing Approximate Entropy (ApEn) method on the Electroencephalogram (EEG) data for effectively identifying the seizure events in real time. The proposed system is initiated with the hardware setup built on Maker Uno microcontroller and BioAmp EXG Pill for EEG data capture and collection by using these open-source software tools. Once the EEG signal is collected, then it is pre-processed to remove noise and artifacts in its. Then the ApEn method is employed for computing the complexity of the EEG data signals by comparing it with each other EEG signals. After that, ApEn value is compared to the threshold value of 0.5 to detect the seizure event or normal brain activity of EEG signals. The seizure event is detected when the ApEn is low, which then activates the Light Emitting Diode (LED) for immediate visual alert to the practitioners. This model offers an improved detection for combining hardware and software to perform the real-time monitoring. Experimental results of this study showcase the effectiveness of the system with the results of higher sensitivity (96.54%), specificity (97.85%), and overall accuracy (9.63%) with minimum demands. This innovative method not only improves the real-time detection of seizure events in the EEG signal data but also shows improved advancements in the field of treatment and patient care.