###### Abstract  
Given the importance of identifying key performance points in organizations, this research intends to determine the most critical intra- and extra-organizational elements in assessing the performance of firms using the European Company Survey (ECS) 2019 framework. The ECS 2019 survey data were used to train an artificial neural network optimized using an imperialist competitive algorithm (ANN-ICA) to forecast business performance and employee wellbeing. In order to assess the correctness of the model, root mean square error (RMSE), mean absolute percentage error (MAPE), mean square error (MSE), correlation coefficient (r), and determination coefficient (R2) have been employed. The mean values of the performance criteria for the impact of internal and external factors on firm performance were 1.06, 0.002, 0.041, 0.9, and 0.83, and the value of the performance metrics for the impact of internal and external factors on employee wellbeing were 0.84, 0.0019, 0.0319, 0.83, and 0.71 (respectively, for MAPE, MSE, RMSE, r, and R2). The great performance of the ANN-ICA model is indicated by low values of MAPE, MSE, and RMSE, as well as high values of r and R2. The outcomes showed that \"skills requirements and skill matching\" and \"employee voice\" are the two factors that matter most in enhancing firm performance and wellbeing. organizational performance; machine learning; big data; imperialist competitive algorithm; employee wellbeing; artificial neural networks; firm performance; artificial intelligence; deep learning; data science +
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by enhancing employee effectiveness, formulating and having a performance evaluation process in place can help companies achieve their objectives [3]. The literature contains evidence that one of the elements impacting the performance of firms is management factors. The organizational ideals and mission are defined by managers who also make them accessible, develop the values necessary for long-term success, and put those values into practice through proper action and behavior. These elements may have a direct or indirect impact on how well a company performs and how it conducts business [4]. Another element whose influence on businesses' success has been researched and verified is human resources [5; 6]. Making people productive in the production and service sectors is crucial for joining international markets and building an economy in the current period of intense market rivalry. Quality, affordability, and speed are the three competitive advantages. In order to improve their innovation culture, several firms opt for a command and control culture [7]. Another factor that significantly affects how well businesses succeed is organizational structure. Synchronizing organizational performance with employee welfare is crucial for improving organizational performance. In recent years, machine learning (ML) techniques have provided valuable tools for evaluating systems [8; 9], modeling proper frameworks [10; 11] and increasing system accuracy [12]. ML-based techniques can successfully link the dependent and independent variables to provide a practical mapping of a system. [PERSON] et al. (2022) suggested that using ML techniques improves business success, and companies should communicate using terms connected to AI, particularly when discussing innovation and teamwork [13]. [PERSON] et al. (2022) employed association rule algorithms, Apriori algorithm, and chi-square automatic interaction detection analysis tree to enhance business performance. This enhancement also provides considerable wellbeing [14]. [PERSON] et al. (2022) employed ML techniques for boosting business performance [15]. As is clear from the literature, ML provides a promising output for analyzing firm performance and employee wellbeing. In the present study, an advanced ML was employed to provide a conceptual framework of firm performance and employee wellbeing.