Adjusted R- Score is very low, How to best fit using the Linear Regression Model

In below Regression results, the R-Squared is too low but p value for some explanatory variables are significant, Is something to be worried about , what steps to be followed for best fit of model by improving R-squared

OLS Regression Results
==============================================================================
Dep. Variable:                  Sales   R-squared:                       0.003
Model:                            OLS   Adj. R-squared:                  0.003
Method:                 Least Squares   F-statistic:                     40.25
Date:                Tue, 26 Oct 2021   Prob (F-statistic):           5.93e-57
Time:                        17:28:47   Log-Likelihood:            -7.5394e+05
No. Observations:               87864   AIC:                         1.508e+06
Df Residuals:                   87856   BIC:                         1.508e+06
Df Model:                           7
Covariance Type:            nonrobust
========================================================================================
                           coef    std err          t      P>|t|      [0.025      0.975]
----------------------------------------------------------------------------------------
const                 1.296e+04   1334.938      9.706      0.000    1.03e+04    1.56e+04
Item_W                   0.5884      1.000      0.588      0.556      -1.372       2.549
Item_Type               -1.2032      0.930     -1.293      0.196      -3.027       0.620
Item_MRP                 0.2944      0.073      4.027      0.000       0.151       0.438
Outlet_Year             -5.5148      0.668     -8.260      0.000      -6.823      -4.206
Outlet_Size             20.0983      6.631      3.031      0.002       7.101      33.096
Outlet_Location_Type   -69.7235      5.810    -12.001      0.000     -81.111     -58.336
Outlet_ID               -3.5718      3.764     -0.949      0.343     -10.950       3.806
==============================================================================
Omnibus:                    14857.798   Durbin-Watson:                   1.996
Prob(Omnibus):                  0.000   Jarque-Bera (JB):            24973.048
Skew:                           1.128   Prob(JB):                         0.00
Kurtosis:                       4.317   Cond. No.                     6.16e+05
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 6.16e+05. This might indicate that there are
strong multicollinearity or other numerical problems.