As humanitarian needs increase while donor budgets decrease, anticipatory strategies are essential for effective crisis response. In this context, machine learning (ML) has emerged as a promising tool for crisis forecasting, offering the potential to support timely interventions and humanitarian decision-making. However, despite rapid developments in ML-based prediction models, questions remain about their practical utility and trustworthiness in real-world humanitarian settings. This study presents a systematic scoping review of 32 academic and gray literature sources to assess the reliability and feasibility of ML systems for conflict forecasting. By analyzing these systems across dimensions such as forecasting scope, data sources, modeling approaches, validation practices, and ethical considerations, the study finds that while some models demonstrate strong predictive performance and methodological rigor, many lack transparent validation, robust error analysis, and operational applicability. The review concludes that while ML systems hold substantial potential for enhancing conflict anticipation, their current real-world readiness is uneven and context-dependent.