This study developed a machine learning-based failure predictive application software to improve the operational efficiency and reliability of turbo-compressors in gas injection plants. The Gas injection plant produced below maximum capacity due to failure problems of the Turbo-compressors, these affected the targeted oil production negatively. The unavailability and unreliable gas plant led to revenue losses. The failure analysis revealed that equipment and material issues, human factors, external factors, and management-related issues contributed to equipment failures. Machine learning techniques, specifically Logistic Regression, Support Vector Machines (SVM), Boosted Trees, and Artificial Neural Networks (ANN), were employed to develop the failure predictive application software. The results showed that the Efficient Linear SVM model achieved a true positive rate of 99.5% for detecting failures and 99.9% classification precision for non-failure events. The Boosted Trees model achieved a true positive rate (TPR) of 99.5% for detecting failures, although it demonstrated a 0.5% false negative rate, highlighting the need for further optimization and integration with ensemble techniques to minimize operational risks. The SVM model further showcased 99.9% classification precision for non-failure events and a minimal false negative occurrence. The nearly perfect R-values across training, validation, and test datasets, coupled with minimal MSE values at the optimal number of epochs displayed by the ANN model further confirmed that the model can generalize effectively to unseen data. The outcomes of this research yielded a highly effective, computationally efficient machine learning-based application software capable of reliably predicting turbo-compressor failures. The study concluded that the developed application software is a powerful tool for predicting failures in gas injection plants, supporting decision-making processes, and enhancing operational safety. Recommendations for future works included refining existing models, exploring additional feature engineering techniques, and evaluating the robustness of the models under varying operational conditions.