For pattern cataloguing and regression issues, the support vector machine (SVM) is an eminent and computationally prevailing machine learning method. It's been effectively addressing several concrete issues across an extensive gamut of domains. SVM possesses a key aspect called penalty factor C. The choice of these aspects has a substantial impact on the classification precision of SVM as unsuitable parameter settings might drive substandard classification outcomes. Penalty factor C is required

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... to achieve an adequate trade-off between classification errors and generalisation performance. Hence, formulating an SVM model having appropriate performance requires parameter optimisation. The simulated annealing (SA) algorithm is employed to formulate a hybrid method for evaluating SVM parameters. Additionally, the intent is to enhance system efficacy to obtain the optimal penalty parameter and balance classification performance at the same time. Our experiments with many UCI datasets indicate that the recommended technique could attain enhanced classification precision.