The electroencephalogram (EEG) signal has several uses in biomedicine, including diseases diagnosis, rehabilitation, brain-computer interfaces, and sleep research, due to its complexity and lack of invasiveness. Researchers have proposed several advanced methods for pre-processing and feature extraction to tackle the complexity of EEG data analysis. This study highlights the importance of EEG to serve the purpose of decoding humans’ thoughts by exploring current methods that help structure brain signals and extract significant information by implementing various effective processing approaches. The discourse encompasses the model architecture of the EEG signal processing, commencing with the initial recording and progressing through data extraction and classification. The study discusses various techniques like Independent Component Analysis, Canonical Correlation Analysis, Discrete Wavelet Transform, and Empirical Mode Decomposition have been developed for various use cases to denoise the EEG signals for accurate data analysis. It also discuss the metrics that evaluates denoising the EEG signals. The studies shows the ability to classify EEG signals using traditional models and deep learning models across multiple domains with the accuracies ranging from 84.2% to 99.3%. Futhermore, the paper confronts the challenges associated with the present technologies and looks at upcoming progressions. Additionally, it provides several suggestions for future research initiatives within this domain.