This study explores the high-dimensional multi-scale flow dynamics of a multiple-input and multiple-output configuration, the fluidic pinball, at Re=100. A data-driven classification approach is used to categorize flow dynamics into steady, periodic, and chaotic regimes by clustering. The key enabler is the parameterization of the control variables using three actuation parameters, simplifying the analysis and identification of flow regime transitions. The results highlight the effects of changes in the three actuation parameters on flow stability and transitions, emphasizing how boat-tailing, Magnus effect, and stagnation point actuation influence flow structure. A transition diagram maps the steady-to-chaotic scenario, revealing smooth boundaries for steady-to-periodic transitions and complex, fractal-like boundaries for transitions to chaos. This work enhances understanding and parametric modeling capabilities for the actuated flow dynamics, contributing to more effective control strategies in engineering applications involving bluff body flows and multiple-input systems.