The early prediction of software failure is important in the field of software engineering since it leads to the development of better quality software, along with a reduction in maintenance cost and effort. However, even though there is growing interest in early prediction of software failure, the existing literature shows some gaps. While many studies are quite reliant on static code metrics or test case execution data, they tend to miss out on vital dynamic and contextual information which can be obtained by analyzing software system logs. Log data is regularly created by computing systems during their runtime and contains rich information including event sequences, timestamps, error messages, and system states that can potentially being utilized in the identification of anomalies and predictions of failures on real-time. The objective of this work is to categorize the existing literature on the use of system logs for predicting software, through systematic literature review, with the help of the guidelines from Barbara Kitchenham. The review categorizes system logs into four primary parameters: resource/hardware logs, workload/performance logs, network logs, and security logs. It also highlights the machine learning models. The findings reveal that log attributes such as CPU usage, memory utilization, disk space, transaction processing, and network errors are consistently identified as key predictors of software failure. This finding aligns with expert opinions, demonstrating strong agreement on the relevance of these attributes for predicting software failure. This study contributes to the growing body of knowledge on software failure prediction, emphasizing the importance of integrating machine learning with systematic log monitoring to enhance proactive system failure management. Future work should focus on developing real-time monitoring tools that leverage machine learning models to automate failure detection and prediction across various system components.