This study evaluates and compares several machine learning methods on the effects of different parameters in the hydrothermal carbonisation (HTC) process of macroalgae Sargassum horneri. Reaction temperature, residence time, biomass particle size, the amount of catalyst and loaded biomass were considered as inputs and three variables of BET, higher heating value (HHV) and energy recovery were regarded as outputs. For analysing the input parameters, the Taguchi method was used for experimental designs and the obtained results were utilised here as training sets. Various data mining approaches like support vector machine (SVM), group method of data handling, decision tree, random forest, radial basis function, adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron (MLP) neural network were implemented to model the problem and two different optimisation techniques named BAT and Grasshopper Optimisation Algorithm (GOA) were employed with MLP and ANFIS model for optimising. By comparing different statistical parameters such as Average Absolute Relative Deviation (AARD), coefficient of determination (R-2), Root Mean Square Error (RMSE) and Standard Deviation (SD), It is found out that SVM method has a considerably better performance relative to other methods for estimating both the BET, HHV and energy recovery parameters. Furthermore, coupling of MLP and ANFIS with GOA increases the accuracy of these models for BET, HHV and energy recovery estimations.