<b>Machine Learning Adoption Among Small and Medium Enterprises in Sierra Leone: Awareness, Perceived Benefits, and Barriers</b>

Small and Medium Enterprises (SMEs) are the pillars of Sierra Leone's economy, contributing to employment and poverty reduction, yet confront persistent structural challenges including limited finance access and weak market intelligence. Machine Learning (ML) offers transformative possibilities to address these challenges, but its projection into Sierra Leone's SME sector remains empirically unprevailed. This cross-sectional study surveyed (141) SME operators across Sierra Leone, analysed via descriptive statistics, chi-square tests, and logistic regression. Findings show 63.1% had no prior ML awareness, while 90.1% expressed willingness to adopt affordable ML tools. Customer service (26.2%), sales forecasting (17.0%), and inventory management (16.3%) were the highest impact areas; principal barriers were lack of knowledge (24.1%), poor connectivity (22.7%), and high technology costs (22.0%). ML awareness was significantly associated with perceived competitive advantage (p = 0.001). SME operators demonstrate high receptiveness to ML adoption, but realising this requires coordinated involvement addressing infrastructure, financial, and capacity gaps.