Logs are crucial in software development and maintenance, enabling developers to analyze system activities and troubleshoot issues. Traditional manual log analysis, which relies on human proficiency, is increasingly impractical due to the sheer volume and complexity of modern software-generated logs. This article explores how machine learning (ML) offers a solution by automating log analysis, enabling rapid data categorization, automatic issue detection, and early anomaly detection, thus allowing engineers to focus on more complex tasks. Several ML-powered log analysis tools, such as Coralogix, Datadog, and Splunk, provide diverse functionalities to enhance monitoring, alerting, and data visualization. The choice of tool should consider factors beyond basic functionalities and budget, such as the time saved by adopting a comprehensive, out-of-the-box solution for log management.