The transport spot rate in trucking logistics is an important factor for market participants in the recycling industry. Knowledge about the current spot rate is essential for operational decision-making in price negotiations between brokers and shippers. Due to the characteristics and dynamics of the industry, this task is particularly challenging. So far, businesses mainly rely on traditional calculation methods combined with their own expertise in price negotiations. The growing amount of existing business and market data may enable companies to take advantage of data-driven decision processes. However, the resulting volume of data and required effort for analysis do not match the fast pace of daily business. To improve current forecasting practices, this paper conducts a comparative study of machine learning (ML) approaches for shipment-specific spot rate prediction. For this, the paper builds on the experience and database of a small broker in the recycling industry in Northern Germany and complements it with external market information. The study shows the ability of ML to internalize underlying patterns between spot rates and market data. During the use case the CRISP-DM framework is followed to select the most appropriate features and train multiple ML algorithms. Several metrics are applied to determine the most accurate model for spot rate prediction. Results indicate that especially the ML-algorithm Random Forest shows considerable potential to provide brokers in the recycling industry with more reliable spot rate assumptions. Therefore, future implementation of ML approaches in the industry may open up new and beneficial business opportunities. The study paths the way for further research on the predictive potential of ML for prices in transportation with extended and diversified data sets.