Recent breakthroughs in machine learning (ML) have enabled unprecedented advances across highly diverse application domains, including financial risk prediction, environmental forecasting, physiological diagnostics, and human behavioral sensing. Multi-domain learning (MDL) involves training multiple specialized models for different domains, necessitating significant labeling effort by human experts. To mitigate this, active learning (AL) can be employed to focus on the most informative data, leading to the concept of multi-domain active learning (MDAL). This work presents a comprehensive literature review of MDAL, highlighting the limited applicability of existing studies to broader MDAL tasks. A pipeline for MDAL is developed, featuring a comparative study of thirty algorithms derived from six MDL models and five AL strategies, evaluated on six datasets across textual and visual classification tasks. The findings indicate that AL generally enhances MDL performance, with the naive BvSB (best vs. second best) Uncertainty strategy being competitive with leading AL techniques. Notably, BvSB combined with the multinomial adversarial networks (MAN) model consistently achieves top performance across all datasets. Qualitative analyses reveal insights into the effective behaviors of these strategies, and BvSB with the MAN model is recommended for MDAL applications due to its strong experimental performance. We propose a novel Unified Multi-Domain Prediction Framework (UMPF) leveraging multimodal feature fusion, hybrid ensemble learning, and transformer-based deep models to understand shared patterns across domains with distinct distributions. Experimental results demonstrate that UMPF achieves substantial improvements in accuracy (+6–18%), robustness (+12% in noisy environments), and computational efficiency compared to single-domain baselines. The findings reveal fundamental similarities—such as temporal correlations, anomaly signatures, and multidimensional risk factors shared across credit card fraud detection, rainfall prediction, emotion/activity classification, and heart disease diagnosis. This paper contributes new theoretical, methodological, and empirical insights toward designing ML systems capable of general-purpose intelligence across heterogeneous real-world tasks.