A methodology based on permutation entropy and Lempel-Ziv complexity to characterize plasma fluctuations from the Santander Linear Plasma Machine is presented. These information-theoretic and symbolic dynamics tools are used to analyze temporal records and quantify randomness and structural complexity. The analysis reveals that the signals analyzed consistently occupy an intermediate region in the entropy-complexity plane, indicating low-dimensional chaotic behavior. This is supported by comparisons with synthetic signals, including white noise, periodic sequences, and outputs from chaotic maps. The pipeline involves preprocessing, symbolic encoding, and statistical validation, and is implemented using open-source tools in Python and MATLAB. The framework enables effective regime classification and anomaly detection in experimental plasma data, even under limited data conditions.