The prompt detection of trace amounts of antibiotics is crucial for environmental surveillance and the entire ecosystem. An electrochemical sensor has been designed to precisely detect the concentration of ciprofloxacin (CIP) utilizing gold nanoparticles coated with chitosan on a glassy carbon electrode (AuNPs/CHI/GCE). The influence of four key electrochemical parameters, viz., scan rate, pH, temperature, and potential, on the satisfactory detection of CIP was also studied. The accumulated analysis generated one output parameter, which was current intensity, resulting in 2319 data points. This dataset was then used in machine learning models to predict the maximum cathodic current for CIP detection. The best model and its optimal settings of parameters were determined after comparing ten different ML models: AdaBoost, Bagging, CatBoost, Decision Tree, Extra Trees, Gradient Boosting, HistGradientBoosting, LightGBM, RandomForest, and XGBoost, the performance of the models has been evaluated by metrics Coefficient of Determination (R²), Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Median Absolute Error (MedAE). It was concluded that CatBoost is the best model both on cumulative performance and final metrics results. These include an R² score of 0.9985, MSE of 1.468, RMSE of 1.225, MAE of 0.672, and MedAE of 0.375. Under optimal conditions, the cathodic current for CIP detection by the CB Model was approximately 128.51 μA, with a potential of 0.47 V, scan rate of 98 mV/sec, temperature of 310 C, and pH of the reaction medium at 6.75. These parameters were further used in constructing a calibration curve using differential pulse voltammetry (DPV), accurately determining the presence of CIP in known and unknown samples. The LOD, LOQ and the specific sensitivity of the developed sensor were 0.049, 0.232 μM, and 786.72 µA µM−1 cm−2 respectively. The results show that the developed electrode can quickly and accurately determine ciprofloxacin in water samples, which can be useful for fast and sensitive analysis.