Abstract: Accurate profit forecasting is critical for small-scale pharmacies, particularly in resource-constrained environments where financial decisions must be both timely and data-informed. This study investigates the predictive performance of sixteen regression models for daily profit forecasting using transactional data collected from a single local pharmacy in Sabha, Libya, over a 14-month period. An exploratory data analysis revealed strong right-skewed distributions in sales, cost, and profit, as well as pronounced temporal patterns, including seasonal peaks during spring and early summer and weekly profit clustering around weekends. After outlier treatment using the interquartile range method. A total of sixteen regression models were developed and evaluated, encompassing linear models (Linear, Ridge, Lasso, ElasticNet), tree-based models (Decision Tree, Random Forest, Extra Trees, Gradient Boosting, AdaBoost), proximity-based models (K-Nearest Neighbors), kernel-based models (Support Vector Regression), and neural architectures (Multi-Layer Perceptron, Convolutional Neural Network, Long Short-Term Memory, Gated Recurrent Unit). The models were assessed using Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, and the R-squared score. The results consistently showed that tree-based ensemble models—particularly Extra Trees and LightGBM—achieved the highest accuracy, with R² values of 0.978 and 0.975 respectively, significantly outperforming neural and linear models. Learning curves and residual plots further confirmed the superior generalization and robustness of these models. We acknowledge that the dataset size (424 records) and the deterministic relationship between sales, costs, and profit influence these metrics. The study highlights the importance of model selection tailored to domain-specific data characteristics and suggests that well-tuned ensemble methods may offer reliable, interpretable, and scalable solutions for profit forecasting in simialr low-resource retail environments. However, broad claims of usefulness for all low-resource settings should be tempered by the limited scope of this dataset. Future work should consider longer-term data and external economic indicators to further improve model reliability, and focus on operational deployment strategies, investigating how these models can be integrated into daily pharmacy workflows despite real-time data constraints.