Cerebral microbleeds (CMB) are small hypointense lesions visible on gradient echo (GRE) or susceptibility-weighted (SWI) MRI, serving as critical biomarkers for various cerebrovascular and neurological conditions. Accurate quantification of CMB is essential, as their number correlates with the severity of conditions such as small vessel disease, stroke risk and cognitive decline. Current detection methods depend on manual inspection, which is time-consuming and prone to variability. Automated detection using deep learning presents a transformative solution but faces challenges due to the heterogeneous appearance of CMB, high false-positive rates, and similarity to other artefacts. This study investigates the application of deep learning techniques to public (ADNI and AIBL) and private datasets (OATS and MAS), leveraging GRE and SWI MRI modalities to enhance CMB detection accuracy, reduce false positives, and ensure robustness in both clinical and normal cases (i.e., scans without cerebral microbleeds). A 3D convolutional neural network (CNN) was developed for automated detection, complemented by a You Only Look Once (YOLO)-based approach to address false positive cases in more complex scenarios. The pipeline incorporates extensive preprocessing and validation, demonstrating robust performance across a diverse range of datasets. The proposed method achieves remarkable performance across four datasets, ADNI: Balanced accuracy: 0.953, AUC: 0.955, Precision: 0.954, Sensitivity: 0.920, F1-score: 0.930, AIBL: Balanced accuracy: 0.968, AUC: 0.956, Precision: 0.956, Sensitivity: 0.938, F1-score: 0.946, MAS: Balanced accuracy: 0.889, AUC: 0.889, Precision: 0.948, Sensitivity: 0.779, F1-score: 0.851, and OATS dataset: Balanced accuracy: 0.93, AUC: 0.930, Precision: 0.949, Sensitivity: 0.862, F1-score: 0.900. These results highlight the potential of deep learning models to improve early diagnosis and support treatment planning for conditions associated with CMB.